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Mario Casciaro is a software engineer and technical lead with a passion for open source. He began programming with a Commodore 64 when he was 12, and grew up with Pascal and Visual Basic. His programming skills evolved by experimenting with x86 assembly language, C, C++, PHP, and Java. His relentless work on side projects led him to discover JavaScript and Node.js, which quickly became his new passion. Mario now works in a lighthouse at D4H Technologies, where he led the development of a real-time platform to manage emergency operations (Node.js to save lives!). As Mario is at the cutting-edge of Node development, we asked him to share his thoughts on the future of his field, and also on what led him to want to write a book with Packt.   Why did you decide to write with Packt, what convinced you? I already knew Packt from some excellent books I read in the past and that was certainly a good start. As an author I was given a lot of freedom in the definition of the contents of my book and this was probably what I liked the most as it allowed me to leverage my knowledge and passion at its fullest. Besides that, Packt has one of the best royalties package out there, something to not overlook. As a first-time Packt author, what type of support did you receive? Packt provided me with some interesting education material, and besides the mandatory style guide, there were some articles with advice on how to organize the work and deal with the ups and downs of the writing process. However, what was most helpful was the patience of the editors in providing feedback and fixes, especially during the writing of the initial chapters. I think this is a make-or-break factor as the first 2/3 months are probably the toughest and it's important to have the support of an empathetic team to succeed. What were your main aims when you began writing? My main goal was to write a book worth reading, something that I would have bought and read myself if I wasn't the author. One of the things I wanted to avoid was to include trivial content, knowledge that could easily be found elsewhere, such as in a good free tutorial or in some official documentation. I wanted every topic to teach the reader something new, I wanted to 'wow' the reader with content and notions it was unlikely they knew before. What was the most rewarding part of the writing experience? Reading a message from a reader saying 'thank you' is probably the most rewarding part of being an author. It brightens up my day and reminds me that the time writing the book was well spent. In general, knowing that I helped somebody learn something new and valuable is an amazing feeling. What do you see as the next big thing in your field, and what developments are you excited about? JavaScript is taking over the world, it is spreading well beyond the browser and the web. This change is already happening, we can use JavaScript to implement server applications (node.js), in databases (couchdb), in connected devices (tessel), on mobiles (phonegap) and on desktops (nw.js). I'm also looking forward to the spreading of the new ES6 and ES7 standards which should add even more powerful features to the language. Do you have any advice for new authors? Dream big but keep it simple. If you want to explain complex topics always start from the basic notions and build on top of those as you move forward. Always assume there might be some reader that doesn't have a prior knowledge to completely grasp a concept or a code sample. Spend a few words on the background of a problem/solution, why the reader should learn it and what advantages it brings. No one likes to be fed with knowledge without knowing why. On the organizational side, I would say that the most important aspect is sticking with the schedule, no matter what. Build the habit of writing for the book at a given time of the day and move away from the computer screen only when you spent all your mental energy. For me, that was the most rewarding moment of the day, knowing that I’ve done my job and that I couldn’t have done more. Always focus on the current chapter, try to get it as much production-ready as you can (there will be little time to change things later), don’t think to the amount of work left to finish the book, one chapter at the time you will have achieved what you previously considered impossible. Follow Mario on Twitter, connect with him on Linkedin, or find him on Github. You can buy Mario’s book Node.js Design Patterns from packtpub or find out more about the book here: http://www.nodejsdesignpatterns.com If you have been inspired to write, get in touch with our experienced team who are always open to discussing and designing potential book ideas with talented people. Click here for more details.

IntroductionNavigating the world of data and AI systems can be overwhelming.Their complexity often makes it difficult to visualize how data engineering, research (data science and machine learning), and production roles (AI engineering, ML engineering, MLOps) work together to form an end-to-end system.As a data engineer, your work finishes when standardized data is ingested into a data warehouse or lake.As a researcher, your work ends after training the optimal model on a static dataset and registering it.As an AI or ML engineer, deploying the model into production often signals the end of your responsibilities.As an MLOps engineer, your work finishes when operations are fully automated and adequately monitored for long-term stability.But is there a more intuitive and accessible way to comprehend the entire end-to-end data and AI ecosystem?Absolutely—through the FTI architecture.Let’s quickly dig into the FTI architecture and apply it to a production LLM & RAG use case. Figure 1: The mess of bringing structure between the common elements of an ML system.Introducing the FTI architectureThe FTI architecture proposes a clear and straightforward mind map that any team or person can follow to compute the features, train the model, and deploy an inference pipeline to make predictions.The pattern suggests that any ML system can be boiled down to these 3 pipelines: feature, training, and inference.This is powerful, as we can clearly define the scope and interface of each pipeline. Ultimately, we have just 3 instead of 20 moving pieces, as suggested in Figure 1, which is much easier to work with and define.Figure 2 shows the feature, training, and inference pipelines. We will zoom in on each one to understand its scope and interface.Figure 2: FTI architectureBefore going into the details, it is essential to understand that each pipeline is a separate component that can run on different processes or hardware. Thus, each pipeline can be written using a different technology, by a different team, or scaled differently.The feature pipelineThe feature pipeline takes raw data as input, processes it, and outputs the features and labels required by the model for training or inference.Instead of directly passing them to the model, the features and labels are stored inside a feature store. Its responsibility is to store, version, track, and share the features.By saving the features into a feature store, we always have a state of our features. Thus, we can easily send the features to the training and inference pipelines.The training pipelineThe training pipeline takes the features and labels from the features stored as input and outputs a trained model(s).The models are stored in a model registry. Its role is similar to that of feature stores, but the model is the first-class citizen this time. Thus, the model registry will store, version, track, and share the model with the inference pipeline.The inference pipelineThe inference pipeline takes as input the features and labels from the feature store and the trained model from the model registry. With these two, predictions can be easily made in either batch or real-time mode.As this is a versatile pattern, it is up to you to decide what you do with your predictions. If it’s a batch system, they will probably be stored in a DB. If it’s a real-time system, the predictions will be served to the client who requested them.The most important thing you must remember about the FTI pipelines is their interface. It doesn’t matter how complex your ML system gets — these interfaces will remain the same.The final thing you must understand about the FTI pattern is that the system doesn’t have to contain only 3 pipelines. In most cases, it will include more.For example, the feature pipeline can be composed of a service that computes the features and one that validates the data. Also, the training pipeline can comprise the training and evaluation components.Applying the FTI architecture to a use caseThe FTI architecture is tool-agnostic, but to better understand how it works, let’s present a concrete use case and tech stack.Use case: Fine-tune an LLM on your social media data (LinkedIn, Medium, GitHub) and expose it as a real-time RAG application. Let’s call it your LLM Twin.As we build an end-to-end system, we split it into 4 pipelines:The data collection pipeline (owned by the DE team)The FTI pipelines (owned by the AI teams)As the FTI architecture defines a straightforward interface, we can easily connect the data collection pipeline to the ML components through a data warehouse, which, in our case, is a MongoDB NoSQL DB.The feature pipeline (the second ML-oriented data pipeline) can easily extract standardized data from the data warehouse and preprocess it for fine-tuning and RAG.The communication between the two is done solely through the data warehouse. Thus, the feature pipeline isn’t aware of the data collection pipeline and how it collected the raw data. Figure 3: LLM Twin high-level architectureThe feature pipeline does two things:chunks, embeds and loads the data to a Qdrant vector DB for RAG;generates an instruct dataset and loads it into a versioned ZenML artifact.The training pipeline ingests a specific version of the instruct dataset, fine-tunes an open-source LLM from HuggingFace, such as Llama 3.1, and pushes it to a HuggingFace model registry.During the research phase, we use a Comet ML experiment tracker to compare multiple fine-tuning experiments and push only the best one to the model registry.During production, we can automate the training job and use our LLM evaluation strategy or canary tests to check if the new LLM is fit for production.As the input dataset and output model registry are decoupled, we can quickly launch our training jobs using ML platforms like AWS SageMaker.ZenML orchestrates the data collection, feature, and training pipelines. Thus, we can easily schedule them or run them on demand orThe end-to-end RAG application is implemented in the inference pipeline side, which accesses fresh documents from the Qdrant vector DB and the latest model from the HuggingFace model registry.Here, we can implement advanced RAG techniques such as query expansion, self-query and rerank to improve the accuracy of our retrieval step for better context during the generation step.The fine-tuned LLM will be deployed to AWS SageMaker as an inference endpoint. Meanwhile, the rest of the RAG application is hosted as a FastAPI server, exposing the end-to-end logic as REST API endpoints.The last step is to collect the input prompts and generated answers with a prompt monitoring tool such as Opik to evaluate the production LLM for things such as hallucinations, moderation or domain-specific problems such as writing tone and style.SummaryThe FTI architecture is a powerful mindmap that helps you connect the dots in the complex data and AI world, as illustrated in the LLM Twin use case.Unlock the full potential of Large Language Models with the "LLM Engineer's Handbook" by Paul Iusztin and Maxime Labonne. Dive deeper into real-world applications, like the FTI architecture, and learn how to seamlessly connect data engineering, ML pipelines, and AI production. With practical insights and step-by-step guidance, this handbook is an essential resource for anyone looking to master end-to-end AI systems. Don’t just read about AI—start building it. Get your copy today and transform how you approach LLM engineering!Author BioPaul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. Paul's determined passion and mission are to build data-intensive AI/ML products that serve the world and educate others about the process. As the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade ML, Paul has significantly enriched the engineering and MLOps community. His weekly content on ML engineering and his open-source courses focusing on end-to-end ML life cycles, such as Hands-on LLMs and LLM Twin, testify to his valuable contributions.

Introduction In the rapidly evolving world of computer graphics, Vulkan has emerged as a powerful and efficient API, revolutionizing how developers approach rendering and compute operations. As the author of "The Modern Vulkan Cookbook," I've had the privilege of diving deep into this technology, exploring its intricacies, and uncovering its potential to solve real-world problems in graphics programming. This book will help you leverage modern graphics programming techniques. You’ll cover a cohesive set of examples that use the same underlying API, discovering Vulkan concepts and their usage in real-world applications.Vulkan, introduced by the Khronos Group in 2016, was designed to address the limitations of older graphics APIs like OpenGL. Its low-overhead, cross-platform nature has made it increasingly popular among developers seeking to maximize performance and gain fine-grained control over GPU resources. One of Vulkan's key strengths lies in its ability to efficiently utilize modern multi-core CPUs and GPUs. By providing explicit control over synchronization and memory management, Vulkan allows developers to optimize their applications for specific hardware configurations, resulting in significant performance improvements. Vulkan Practical Applications Vulkan's impact on solving real-world problems in graphics programming is profound and far-reaching. In the realm of mobile gaming, Vulkan's efficient use of system resources has enabled developers to create console-quality graphics on smartphones, significantly enhancing the mobile gaming experience while conserving battery life. In scientific visualization, Vulkan's compute capabilities have accelerated complex simulations, allowing researchers to process and visualize large datasets in real-time, leading to breakthroughs in fields like climate modeling and molecular dynamics. The film industry has leveraged Vulkan's ray tracing capabilities to streamline pre-visualization processes, reducing production times and costs. In automotive design, Vulkan-powered rendering systems have enabled real-time, photorealistic visualizations of car interiors and exteriors, revolutionizing the design review process. Virtual reality applications built on Vulkan benefit from its low-latency characteristics, reducing motion sickness and improving overall user experience in training simulations for industries like healthcare and aerospace. These practical applications demonstrate Vulkan's versatility in solving diverse challenges across multiple sectors, from entertainment to scientific research and industrial design. Throughout my journey writing "The Modern Vulkan Cookbook," I encountered numerous scenarios where Vulkan's capabilities shine in solving practical challenges: GPU-Driven Rendering: Vulkan's support for compute shaders and indirect drawing commands enables developers to offload more work to the GPU, reducing CPU overhead and improving overall rendering efficiency. This is particularly beneficial for complex scenes with dynamic object counts or procedurally generated geometry. Advanced Lighting and Shading: Vulkan's flexibility in shader programming allows for the implementation of sophisticated lighting models and material systems. Techniques like physically based rendering (PBR) and global illumination become more accessible and performant under Vulkan. Order-Independent Transparency: Achieving correct transparency in real-time rendering has always been challenging. Vulkan's support for advanced rendering techniques, such as A-buffer implementations or depth peeling, provides developers with powerful tools to tackle this issue effectively. Ray Tracing: With the introduction of ray tracing extensions, Vulkan has opened new possibilities for photorealistic rendering in real-time applications. This has profound implications for industries beyond gaming, including architecture visualization and film production. Challenges and Learning Curves Despite its power, Vulkan comes with a steep learning curve. Its verbose nature and explicit control can be daunting for newcomers. During the writing process, I faced the challenge of breaking down complex concepts into digestible chunks without sacrificing depth. This led me to develop a structured approach, starting with core concepts and gradually building up to advanced techniques. One hurdle was explaining the intricacies of Vulkan's synchronization model. Unlike older APIs, Vulkan requires explicit synchronization, which can be a source of confusion and errors for many developers. To address this, I dedicated significant attention to explaining synchronization primitives and their proper usage, providing clear examples and best practices. The Future of Graphics Programming with Vulkan As we look to the future, Vulkan's role in graphics programming is set to grow even further. The API continues to evolve, with new extensions and features being added regularly. Some exciting areas of development include: Machine Learning Integration: The intersection of graphics and machine learning is becoming increasingly important. Vulkan's compute capabilities make it well-suited for implementing ML algorithms directly on the GPU, opening up possibilities for AI-enhanced rendering techniques. Extended Reality (XR): With the rising popularity of virtual and augmented reality, Vulkan's efficiency and low-latency characteristics make it an excellent choice for XR applications. The integration with OpenXR further solidifies its position in this space. Cross-Platform Development: As Vulkan matures, its cross-platform capabilities are becoming more robust. This is particularly valuable for developers targeting multiple platforms, from high-end PCs to mobile devices and consoles. Conclusion Writing "The Modern Vulkan Cookbook" has been an enlightening journey, deepening my appreciation for the power and flexibility of Vulkan. As graphics hardware continues to advance, APIs like Vulkan will play an increasingly crucial role in harnessing this power efficiently. For developers looking to push the boundaries of what's possible in real-time rendering, Vulkan offers a robust toolset. While the learning curve may be steep, the rewards in terms of performance, control, and cross-platform compatibility make it a worthy investment for any serious graphics programmer. Author Bio Preetish Kakkar is a highly experienced graphics engineer specializing in C++, OpenGL, WebGL, and Vulkan. He co-authored "The Modern Vulkan Cookbook" and has extensive experience developing rendering engines, including rasterization and ray-traced pipelines. Preetish has worked with various engines like Unity, Unreal, and Godot, and libraries such as bgfx. He has a deep understanding of the 3D graphics pipeline, virtual/augmented reality, physically based rendering, and ray tracing. 

IntroductionIn today’s data-driven world, businesses rely heavily on robust tools to transform raw data into actionable insights. Among these tools, Microsoft Power BI stands out as a leader, renowned for its versatility and user-friendliness. From its humble beginnings as an Excel add-in, Power BI has evolved into a comprehensive enterprise business intelligence platform, competing with industry giants like Tableau and Qlik. This journey of transformation reflects not only Microsoft’s innovation but also the growing need for accessible, scalable analytics solutions.As a data specialist who has transitioned from traditional data warehousing to modern analytics platforms, I’ve witnessed firsthand how Power BI empowers both technical and non-technical users. It has become an indispensable tool, offering capabilities that bridge the gap between data modeling and visualization, catering to everyone from citizen developers to seasoned data analysts. This article explores the evolution of Power BI, its role in democratizing data analytics, and its integration into broader solutions like Microsoft Fabric, highlighting why mastering Power BI is critical for anyone pursuing a career in analytics.The Changing Tide for Data Analysts When you think of business intelligence in the modern era, Power BI is often the first tool that comes to mind. However, this wasn't always the case. Originally launched as an add-in for Microsoft Excel, Power BI quickly evolved into a comprehensive enterprise business intelligence platform in a few years competing with the likes of Qlik and Tableau—a true testament to its capabilities. As a data specialist, what really impresses me about Power BI's evolution is how Microsoft has continuously improved its user-friendliness, making both data modeling and visualizing more accessible, catering to both technical professionals and business users.  As a data specialist, initially working with traditional data warehousing, and now with modern data lakehouse-based analytics platforms, I’ve come to appreciate the capabilities that Power BI brings to the table. It empowers me to go beyond the basics, allowing me to develop detailed semantic layers and create impactful visualizations that turn raw data into actionable insights. This capability is crucial in delivering truly comprehensive, end-to-end analytics solutions. For technical folk like me, by building on our experiences working with these architectures and the deep understanding of the technologies and concepts that drive them, integrating Power BI into the workflow is a smooth and intuitive process. The transition to including Power BI in my solutions feels almost like a natural progression, as it seamlessly complements and enhances the existing frameworks I work with. It's become an indispensable tool in my data toolkit, helping me to push the boundaries of what's possible in analytics. In recent years, there has been a noticeable increase in the number of citizen developers and citizen data scientists. These are non-technical professionals who are well-versed in their business domains and dabble with technology to create their own solutions. This trend has driven the development of a range of low-code/no-code, visual tools such as Coda, Appian, OutSystems, Shopify, and Microsoft’s Power Platform. At the same time, the role of the data analyst has significantly expanded. More organizations are now entrusting data analysts with responsibilities that were traditionally handled by technology or IT departments. These include tasks like reporting, generating insights, data governance, and even managing the organization’s entire analytics function. This shift reflects the growing importance of data analytics in driving business decisions and operations. As a data specialist, I’ve been particularly impressed by how Power BI has evolved in terms of user-friendliness, catering not just to tech-savvy professionals but also to business users. Microsoft has continuously refined Power BI, simplifying complex tasks and making it easy for users of all skill levels to connect, model, and visualize data. This focus on usability is what makes Power BI such a powerful tool, accessible to a wide range of users. For non-technical users, Power BI offers a short learning curve, enabling them to connect to and model data for reporting without needing to rely on Excel, which they might be more familiar with. Once the data is modeled, they can explore a variety of visualization options to derive insights. Moreover, Power BI’s capabilities extend beyond simple reporting, allowing users to scale their work into a full-fledged enterprise business intelligence system. Many data analysts are now looking to deepen their understanding of the broader solutions and technologies that support their work. This is where Microsoft Fabric becomes essential. Fabric extends Power BI by transforming it into a comprehensive, end-to-end analytics platform, incorporating data lakes, data warehouses, data marts, data engineering, data science, and more. With these advanced capabilities, technical work becomes significantly easier, enabling data analysts to take their skills to the next level and realize their full potential in driving analytics solutions.  If you're considering a career in analytics and business intelligence, it's crucial to master the fundamentals and gain a comprehensive understanding of the necessary skills. With the field rapidly evolving, staying ahead means equipping yourself with the right knowledge to confidently join this dynamic industry. The Complete Power BI Interview Guide is designed to guide you through this process, providing the essential insights and tools you need to jump on board and thrive in your analytics journey. ConclusionConclusionMicrosoft Power BI has redefined the analytics landscape by making advanced business intelligence capabilities accessible to a wide audience, from technical professionals to business users. Its seamless integration into modern analytics workflows and its ability to support end-to-end solutions make it an invaluable tool in today’s data-centric environment. With the rise of citizen developers and expanded responsibilities for data analysts, tools like Power BI and platforms like Microsoft Fabric are paving the way for more innovative and comprehensive analytics solutions.For aspiring professionals, understanding the fundamentals of Power BI and its ecosystem is key to thriving in the analytics field. If you're looking to master Power BI and gain the confidence to excel in interviews and real-world scenarios, The Complete Power BI Interview Guide is an invaluable resource. From the core PowerBI concepts to interview preparation and onboarding tips and tricks, The Complete Power BI Interview Guide is the ultimate resource for beginners and aspiring Power BI job seekers who want to stand out from the competition.Author BioGogula is an analytics and BI architect born and raised in Sri Lanka. His childhood was spent dreaming, while most of his adulthood was and is spent working with technology. He currently works for a technology and services company based out of Colombo. He has accumulated close to 20 years of experience working with a diverse range of customers across various domains, including insurance, healthcare, logistics, manufacturing, fashion, F&B, K-12, and tertiary education. Throughout his career, he has undertaken multiple roles, including managing delivery, architecting, designing, and developing data & AI solutions. Gogula is a recipient of the Microsoft MVP award more than 15 times, has contributed to the development and standardization of Microsoft certifications, and holds over 15 data & AI certifications. In his leisure time, he enjoys experimenting with and writing about technology, as well as organizing and speaking at technology meetups. 

Introduction In the fast-paced world of game development, efficiency is key. Developers often find themselves repeating mundane tasks that can consume valuable time and lead to human error. Imagine if you could automate these repetitive tasks, freeing up your time to focus on more creative aspects of your game. In this article, we’ll explore how creating custom tools in Unity can transform your workflow, boost productivity, and reduce the risk of mistakes. Why Custom Tools Matter Custom tools are tailored solutions that address specific needs in your development process. Here’s why they are crucial: Efficiency: Automate routine tasks to save time and reduce manual effort. Consistency: Ensure that repetitive tasks are executed uniformly across your project. Error Reduction: Minimize human errors by automating processes that are prone to mistakes. Focus on Creativity: Spend more time on innovative aspects of your game rather than getting bogged down with repetitive tasks. Creating Your First Custom Tool Overview: We’ll walk through the process of creating a simple Unity editor tool to automate tasks like aligning game objects or batch renaming assets. 1. Setting Up Your Editor Window Define the Purpose: Clearly outline what your tool will accomplish. Create a New Editor Window: Use Unity’s editor scripting API to create a custom window. Add Basic UI Elements: Incorporate buttons, sliders, or input fields to interact with the tool. 2. Implementing Core Functionality Aligning Game Objects: Write scripts to align selected game objects in the scene. Batch Renaming Assets: Create a script that renames multiple assets based on a naming convention. 3. Tips for Effective Custom Tools Start Simple: Begin with a basic tool and gradually add complexity as needed. Prioritize Usability: Ensure your tool is intuitive and easy to use, even for developers who may not be familiar with the script. Document Your Code: Include comments and documentation to make future updates easier. How Custom Tools Solve Common Issues Custom tools address several common development challenges: Repetitive Tasks: Automate repetitive processes like object alignment or asset management to streamline your workflow. Consistency Issues: Ensure that tasks are performed uniformly across your project, avoiding discrepancies and errors. Time Management: Free up time for more complex and creative aspects of your game development by automating mundane tasks. Let's dive into the hands-on section  We've all encountered broken game objects in our scenes, and manually searching through every object to find missing script references can be tedious and time-consuming. One of the key advantages of editor scripts is the ability to create a tool that automatically scans all game objects and pinpoints exactly where the issues are.  Script Dependency Checker . To use this script, simply place it in the Editor folder within your Assets directory. The script needs to be in the Editor directory to function properly. Here's the code that creates a new menu item, which you'll find in the Editor menu bar. What this script does is invoke the CheckDependencies method, which scans all game objects in the scene, checks for any missing components, and collects them in a list. The results are then displayed through the editor window using the OnGUI function. public class ScriptDependencyChecker : EditorWindow {    private static Vector2 scrollPosition;    private static string[] missingScripts = new string[0];    [MenuItem("Tools/Script Dependency Checker")]    public static void ShowWindow()    {        GetWindow<ScriptDependencyChecker>("Script Dependency Checker");    }    private void OnGUI()    {        if (GUILayout.Button("Check Script Dependencies"))        {            CheckDependencies();        }        if (missingScripts.Length > 0)        {            EditorGUILayout.LabelField("Objects with Missing Scripts:", EditorStyles.boldLabel);            scrollPosition = EditorGUILayout.BeginScrollView(scrollPosition, GUILayout.Height(300));            foreach (var entry in missingScripts)            {                EditorGUILayout.LabelField(entry);            }            EditorGUILayout.EndScrollView();        }        else        {            EditorGUILayout.LabelField("No missing scripts found.");        }    }    private void CheckDependencies()    {        var missingList = new System.Collections.Generic.List<string>();        GameObject[] allObjects = GameObject.FindObjectsOfType<GameObject>();        foreach (var obj in allObjects)        {            var components = obj.GetComponents<Component>();            foreach (var component in components)            {                if (component == null)                {                    missingList.Add($"Missing script on GameObject: {obj.name}");                }            }        }        missingScripts = missingList.ToArray();    } } Now, let's head over to the Unity Editor and start using this tool. As shown in Image 01, you'll find the Script Dependency Checker under Tools | Script Dependency Checker in the menu bar.  Image 01 - Unity’s menu bar When you click on it, a window will open with a button and a debug section that will display any game objects with missing script references, if found. You can see this in Image 02.  Image 02 - Script dependency window After pressing the button, we discovered a game object named AudioManager with a missing script, as shown in Image 03.  Image 03 - Results of the Checker Next, we can search for AudioManager in the hierarchy and address the issue by either reassigning the missing script or removing it entirely if it's no longer needed, as shown in Image 04.   Image 04 - Game Object with missing script Learning More Explore Unity Documentation: Unity’s official documentation provides comprehensive guides on editor scripting. Join Developer Communities: Engage with forums and communities like the Unity Developer Community or Stack Overflow to exchange ideas and get support. Experiment with Examples: Study and modify existing tools to understand their functionality and apply similar concepts to your projects. Conclusion Creating custom tools in Unity not only enhances your productivity but also ensures a smoother and more efficient development process. As you experiment with building and implementing your tools, consider other repetitive tasks that could benefit from automation. Whether it’s organizing project folders or generating procedural content, the possibilities are endless. By leveraging custom tools, you’ll gain more control over your development environment and focus on what truly matters—bringing your game to life. For more insights into Unity development and custom tools, check out my book, Mastering Unity Game Development with C#: Harness the Full Potential of Unity 2022 Game Development Using C#, where you’ll find in-depth guides and practical examples to further enhance your game development skills.  Author BioMohamed Essam is a highly skilled Unity developer with expertise in creating captivating gameplay experiences across various platforms. With a solid background in game development spanning over four years, he has successfully designed and implemented engaging gameplay mechanics for mobile devices and other platforms. His current focus lies in the development of a highly popular multiplayer game, boasting an impressive 20 million downloads. Equipped with a deep understanding of cutting-edge technologies and a knack for creative problem solving, Mohamed Essam consistently delivers exceptional results in his projects.

IntroductionIn today’s digital landscape, scalability isn’t just a buzzword—it’s a crucial determinant of success. As the complexity and user base of applications grow, so do the challenges in designing systems that can efficiently handle massive loads. This ongoing challenge of scalability was a key inspiration for my recent book, “System Design Guide for Software Professionals: Build scalable solutions – from fundamental concepts to cracking top tech company interviews” The Scalability Crisis Consider a scenario where a startup’s web application goes viral, resulting in a massive influx of users. This should be a cause for celebration, but instead, it becomes a nightmare as the application starts to slow down significantly. According to a 2024 report by Ably, nearly 85% of companies that experience sudden user growth face significant performance issues due to scalability challenges. The root cause often lies in early design decisions, where the rush to market overshadows the need to build for scale. The building Blocks Approach Over the years, I've found that the "building blocks" approach to system design is crucial for building scalable systems. This method leverages established patterns and components to improve scalability. Here are some of the key building blocks discussed in my book: Distributed Caching: A report from Ahex shows that implementing distributed caching systems like Redis or Memcached can reduce database load by up to 60%, significantly speeding up read operations. Load Balancing: Modern load balancers are more than just traffic directors; they are intelligent systems that optimize resource utilization. A 2024 NGINX report revealed that effective load balancing can improve server efficiency by 40%, enhancing performance during peak loads. Database Sharding: As data grows, a single database becomes a bottleneck. Sharding allows horizontal scaling, and companies that implemented it have seen up to a 5x increase in database throughput, as noted in a Google Cloud study. Message Queues: Asynchronous processing with message queues like Kafka or RabbitMQ can decouple system components and manage traffic spikes. A Gartner report found that this can lead to a 30% reduction in latency during peak usage times. Content Delivery Networks (CDNs): For global applications, CDNs are essential. According to Cloudflare, CDNs can reduce load times by 50-70% for users across different regions, significantly improving user experience. Real-World Application: Scaling a Hypothetical E-commerce Platform Consider an e-commerce platform initially designed as a monolithic application with a single database. This setup worked well for the first 100,000 users, but performance issues began to surface as the user base grew to a million. Approach: Microservices Architecture: Decomposing the monolith into microservices allows independent scaling of each component. Amazon famously adopted this approach, enabling it to handle billions of requests daily. Distributed Caching: Implementing a distributed cache reduced database queries by 70%, as seen in an Akamai case study. Database Sharding: Sharding the database improved query performance by 80%, according to data from MongoDB. Message Queues: Using message queues for resource-intensive tasks led to a 25% reduction in system load, as per RabbitMQ's benchmarks. CDN Deployment: Deploying a global CDN reduced page load times from 3.5 seconds to under 1 second, similar to the optimizations reported by Shopify. Example Metrics: Before optimization: The average page load time was 3.5 seconds, with 30% of requests exceeding 5 seconds during peak hours. After optimization: Reduced to 800ms, with 99% of requests completing under 2 seconds, even during Black Friday. Database query volume: Reduced by 65% through effective caching strategies. Infrastructure costs: Reduced by 40% while handling 5x more daily active users. The AI/ML Twist: Scaling GenAI Infrastructure Scaling infrastructure for Generative AI (GenAI) presents unique challenges. For instance, consider a startup offering a GenAI service for content creation. Initially, 10 high-end GPUs served 1,000 daily users, processing about 1 million tokens daily. However, rapid growth led to the processing of 500 million tokens per day for 100,000 users. Challenges: GPU Scaling: GPU scaling requires managing expensive, specialized hardware. A BCG report notes that effective GPU utilization can save companies up to 50% in infrastructure costs. Token Economy: The varying token loads in GenAI apps pose significant challenges. Stanford University says token loads can vary dramatically, complicating resource prediction. Cost Management: Cloud GPU instances can cost over $10,000/month. AWS reports that optimized GPU management strategies can reduce costs by 30%. Latency Expectations: Users expect near-instant responses. A study by OpenAI found that sub-second latencies are critical for real-time applications. Solutions: Dynamic GPU Allocation: Implementing dynamic GPU allocation can reduce idle times and costs, as observed by Google Cloud. Request Batching: Grouping user requests can improve GPU throughput by 20%, according to Azure AI. Model Optimization: Techniques like quantization and pruning can reduce model size by 70% and increase inference speed by 50%, as highlighted in MIT’s research. Tiered Service Levels: Offering different response time guarantees can optimize resource allocation, as shown by Microsoft Azure. Distributed Inference: Splitting models across GPUs or using CPU inference can reduce GPU load by 40%, based on Google AI's findings. Example Metrics: Cost per 1000 tokens: Reduced from $0.05 to $0.015 through optimized GPU management. p99 Latency: Improved from 5 seconds to 1.2 seconds. Infrastructure scaling: Handled 1 billion daily tokens with only a 20x increase in costs, compared to the 100x increase projected by traditional scaling methods. Beyond Technology: The Human Factor While technology is critical, fostering a culture of scalability is equally important. A Harvard Business Review article emphasized that companies prioritizing scalable culture from the start are 50% more likely to sustain growth without operational roadblocks. Strategies: Encourage developers to consider scalability from the outset. Invest in monitoring and observability tools to detect issues early. Regularly conduct load tests and capacity planning. Adopt a DevOps culture to break down silos between development and operations. The Road Ahead As we move forward, innovations in edge computing, serverless architectures, and large-scale machine learning will continue to push the boundaries of scalability. However, the foundational principles of scalable system design—modularity, redundancy, and efficient resource utilization—remain vital. By mastering these principles, you can build systems that grow and adapt to an ever-changing digital landscape, whether you’re scaling a web application or pioneering generative AI technologies. Remember, scalability is not a destination but a journey, and having the right building blocks makes all the difference. 

IntroductionThis article provides an in-depth exploration of memory allocation and deallocation in the .NET Common Language Runtime (CLR), covering essential concepts and mechanisms that every .NET developer should understand for optimal application performance. Starting with the fundamentals of stack and heap memory allocation, we delve into how the CLR manages different types of data and the roles these areas play in memory efficiency. We also examine the CLR’s generational garbage collection model, which is designed to handle short-lived and long-lived objects efficiently, minimizing resource waste and reducing memory fragmentation. To help developers apply these concepts practically, the article includes best practices for memory management, such as optimizing object creation, managing unmanaged resources with IDisposable, and leveraging profiling tools. This knowledge equips developers to write .NET applications that are not only memory-efficient but also maintainable and scalable.Understanding Memory Allocation and Deallocation in the .NET Common Language Runtime (CLR) Memory management is a cornerstone of software development, and in the .NET ecosystem, the Common Language Runtime (CLR) plays a pivotal role in how memory is allocated and deallocated. The CLR abstracts much of the complexity involved in memory management, enabling developers to focus more on building applications than managing resources.  Understanding how memory allocation and deallocation work under the hood can help you write more efficient and performant .NET applications. Memory Allocation in the CLR When you create objects in a .NET application, the CLR allocates memory. This process involves several key components, including the stack, heap, and garbage collector. In .NET, memory is allocated in two main areas: the stack and the heap. Stack Allocation: The stack is a Last-In-First-Out (LIFO) data structure for storing value types and method calls. Variables stored on the stack are automatically managed, meaning that when a method exits, all its local variables are popped off the stack, and the memory is reclaimed. This process is very efficient because the stack operates linearly and predictably. Heap Allocation: On the other hand, the heap is used for reference types (such as objects and arrays). Memory on the heap is allocated dynamically, meaning that the size and lifespan of objects are not known until runtime. When you create a new object, memory is allocated on the heap, and a reference to that memory is returned to the stack where the reference type variable is stored. When a .NET application starts, the CLR reserves a contiguous block of memory called the managed heap. This is where all reference-type objects are stored. The managed heap is divided into three generations (0, 1, and 2), which are part of the Garbage Collector (GC) strategy to optimize memory management: Generation 0: Short-lived objects are initially allocated here. This is typically where small and temporary objects reside. Generation 1: Acts as a buffer between short-lived and long-lived objects. Objects that survive a garbage collection in Generation 0 are promoted to Generation 1. Generation 2: Long-lived objects like static data reside here. Objects that survive multiple garbage collections are eventually moved to this generation. When a new object is created, the CLR checks the available space in Generation 0 and allocates memory for the object. If Generation 0 is full, the GC is triggered to reclaim memory by removing objects that are no longer in use. Memory Deallocation and Garbage Collection The CLR’s garbage collector is responsible for reclaiming memory by removing inaccessible objects in the application. Unlike manual memory management, where developers must explicitly free memory, the CLR automatically manages this through garbage collection, which simplifies memory management but requires an understanding of how and when this process occurs. Garbage collection in the CLR involves three main steps: Marking: The GC identifies all objects still in use by following references from the root objects (such as global and static references, local variables, and CPU registers). Any objects not reachable from these roots are considered garbage. Relocating: The GC then updates the references to the surviving objects to ensure that they point to the correct locations after compacting memory. Compacting: The memory occupied by the unreachable (garbage) objects is reclaimed, and the remaining objects are moved closer together in memory. This compaction step reduces fragmentation and makes future memory allocations more efficient. The CLR uses the generational approach to garbage collection in .NET, designed to optimize performance by reducing the amount of memory that needs to be examined and reclaimed.  Generation 0 collections occur frequently but are fast because most objects in this generation are short-lived and can be quickly reclaimed. Generation 1 collections are less frequent but handle objects that have survived at least one garbage collection. Generation 2 collections are the most comprehensive and involve long-lived objects that have survived multiple collections. These collections are slower and more resource-intensive. Best Practices for Managing Memory in .NET Understanding how the CLR handles memory allocation and deallocation can guide you in writing more efficient code. Here are a few best practices: Minimize the Creation of Large Objects: Large objects (greater than 85,000 bytes) are allocated in a special section of the heap called the Large Object Heap (LOH), which is not compacted due to the overhead associated with moving large blocks of memory. Large objects should be used judiciously because they are expensive to allocate and manage.  Use `IDisposable` and `using` Statements: Implementing the `IDisposable` interface and using `using` statements ensures that unmanaged resources are released promptly. Profile Your Applications: Regularly use profiling tools to monitor memory usage and identify potential memory leaks or inefficiencies. Conclusion Mastering memory management in .NET is essential for building high-performance, reliable applications. By understanding the intricacies of the CLR, garbage collection, and best practices in memory management, you can optimize your applications to run more efficiently and avoid common pitfalls like memory leaks and fragmentation. Effective .NET Memory Management, written by Trevoir Williams, is your essential guide to mastering the complexities of memory management in .NET programming. This comprehensive resource equates developers with the tools and techniques to build memory-efficient, high-performance applications.  The book delves into fundamental concepts like: Memory Allocation and Garbage Collection Memory profiling and Optimization Strategies  Low-level programming with Unsafe Code Through practical examples and best practices, you’ll learn how to prevent memory leaks, optimize resource usage, and enhance application scalability. Whether you’re developing desktop, web, or cloud-based applications, this book provides the insights you need to manage memory effectively and ensure your .NET applications run smoothly and efficiently. Author BioTrevoir Williams, a passionate software and system engineer from Jamaica, shares his extensive knowledge with students worldwide. Holding a Master&rsquo;s degree in Computer Science with a focus on Software Development and multiple Microsoft Azure Certifications, his educational background is robust. His diverse experience includes software consulting, engineering, database development, cloud systems, server administration, and lecturing, reflecting his commitment to technological excellence and education. He is also a talented musician, showcasing his versatility. He has penned works like Microservices Design Patterns in .NET and Azure Integration Guide for Business. His practical approach to teaching helps students grasp both theory and real-world applications.

Introduction In the rapidly evolving field of Natural Language Processing (NLP), staying ahead of technological advancements while mastering foundational principles is crucial for professionals aiming to drive innovation. "Mastering NLP from Foundations to LLMs" by Packt Publishing serves as a comprehensive guide for those seeking to deepen their expertise. Authored by leading figures in Machine Learning and NLP, this text bridges the gap between theoretical knowledge and practical applications. From understanding the mathematical underpinnings to implementing sophisticated NLP models, this book equips readers with the skills necessary to solve today’s complex challenges. With insights into Large Language Models (LLMs) and emerging trends, it is an essential resource for both aspiring and seasoned NLP practitioners, providing the tools needed to excel in the data-driven world of AI. In-Depth Analysis of Technology NLP is at the forefront of technological innovation, transforming how machines interpret, generate, and interact with human language. Its significance spans multiple industries, including healthcare, finance, and customer service. At the core of NLP lies a robust integration of foundational techniques such as linear algebra, statistics, and Machine Learning. Linear algebra is fundamental in converting textual data into numerical representations, such as word embeddings. Statistics play a key role in understanding data distributions and applying probabilistic models to infer meaning from text. Machine Learning algorithms, like decision trees, support vector machines, and neural networks, are utilized to recognize patterns and make predictions from text data. "Mastering NLP from Foundations to LLMs" delves into these principles, providing extensive coverage on how they underpin complex NLP tasks. For example, text classification leverages Machine Learning to categorize documents, enhancing functionalities like spam detection and content organization. Sentiment analysis uses statistical models to gauge user opinions, helping businesses understand consumer feedback. Chatbots combine these techniques to generate human-like responses, improving user interaction. By meticulously elucidating these technologies, the book highlights their practical applications, demonstrating how foundational knowledge translates to solving real-world problems. This seamless integration of theory and practice makes it an indispensable resource for modern tech professionals seeking to master NLP. Adjacent Topics The realm of NLP is witnessing groundbreaking advancements, particularly in LLMs and hybrid learning paradigms that integrate multimodal data for richer contextual understanding. These innovations are setting new benchmarks in text understanding and generation, driving enhanced applications in areas like automated customer service and real-time translation. "Mastering NLP from Foundations to LLMs" emphasizes best practices in text preprocessing, such as data cleaning, normalization, and tokenization, which are crucial for improving model performance. Ensuring robustness and fairness in NLP models involves techniques like resampling, weighted loss functions, and bias mitigation strategies to address inherent data disparities. The book also looks ahead at future directions in NLP, as predicted by industry experts. These include the rise of AI-driven organizational structures where decentralized AI work is balanced with centralized data governance. Additionally, there is a growing shift towards smaller, more efficient models that maintain high performance with reduced computational resources. "Mastering NLP from Foundations to LLMs" encapsulates these insights, offering a forward-looking perspective on NLP and providing readers with a roadmap to stay ahead in this rapidly advancing field. Problem-Solving with Technology "Mastering NLP from Foundations to LLMs" addresses several critical issues in NLP through innovative methodologies. The book first presents common workflows with LLMs such as prompting via APIs and building a Langchain pipeline. From there, the book takes on heavier challenges. One significant challenge is managing multiple models and optimizing their performance for specific tasks. The book introduces the concept of using multiple LLMs in parallel, with each model specialized for a particular function, such as a medical domain or backend development in Python. This approach reduces overall model size and increases efficiency by leveraging specialized models rather than a single, monolithic one. Another issue is optimizing resource allocation. The book discusses strategies like prompt compression for cost reduction, which involves compacting input prompts to minimize token count without sacrificing performance. This technique addresses the high costs associated with large-scale model deployments, offering businesses a cost-effective way to implement NLP solutions. Additionally, the book explores fault-tolerant multi-agent systems using frameworks like Microsoft’s AutoGen. By assigning specific roles to different LLMs, these systems can work together to accomplish complex tasks, such as professional-level code generation and error checking. This method enhances the reliability and robustness of AI-assisted solutions. Through these problem-solving capabilities, "Mastering NLP from Foundations to LLMs" provides practical solutions that make advanced technologies more accessible and efficient for real-world applications. Unique Insights and Experiences Chapter 11 of "Mastering NLP from Foundations to LLMs" offers a wealth of expert insights that illuminate the future of NLP. Contributions from industry leaders like Xavier Amatriain (VP, Google) and Nitzan Mekel-Bobrov (CAIO, Ebay) explore hybrid learning paradigms and AI integration into organizational structures, shedding light on emerging trends and practical applications. The authors, Lior Gazit and Meysam Ghaffari, share their personal experiences of implementing NLP technologies in diverse sectors, ranging from finance to healthcare. Their journey underscores the importance of a solid foundation in mathematical and statistical principles, combined with innovative problem-solving approaches. This book empowers readers to tackle advanced NLP challenges by providing comprehensive techniques and actionable advice. From addressing class imbalances to enhancing model robustness and fairness, the authors equip practitioners with the skills needed to develop robust NLP solutions, ensuring that readers are well-prepared to push the boundaries of what’s possible in the field. Conclusion "Mastering NLP from Foundations to LLMs" is an 11-course meal that offers a comprehensive journey through the intricate landscape of NLP. It serves as both a foundational text and an advanced guide, making it invaluable for beginners seeking to establish a solid grounding and experienced practitioners aiming to deepen their expertise. Covering everything from basic mathematical principles to advanced NLP applications like LLMs, the book stands out as an essential resource. Throughout its chapters, readers gain insights into practical problem-solving strategies, best practices in text preprocessing, and emerging trends predicted by industry experts. "Mastering NLP from Foundations to LLMs" equips readers with the skills needed to tackle advanced NLP challenges, making it a comprehensive, indispensable guide for anyone looking to master the evolving field of NLP. For detailed guidance and expert advice, dive into this book and unlock the full potential of NLP techniques and applications in your projects. Author BioLior Gazit is a highly skilled Machine Learning professional with a proven track record of success in building and leading teams drive business growth. He is an expert in Natural Language Processing and has successfully developed innovative Machine Learning pipelines and products. He holds a Master degree and has published in peer-reviewed journals and conferences. As a Senior Director of the Machine Learning group in the Financial sector, and a Principal Machine Learning Advisor at an emerging startup, Lior is a respected leader in the industry, with a wealth of knowledge and experience to share. With much passion and inspiration, Lior is dedicated to using Machine Learning to drive positive change and growth in his organizations.Meysam Ghaffari is a Senior Data Scientist with a strong background in Natural Language Processing and Deep Learning. Currently working at MSKCC, where he specialize in developing and improving Machine Learning and NLP models for healthcare problems. He has over 9 years of experience in Machine Learning and over 4 years of experience in NLP and Deep Learning. He received his Ph.D. in Computer Science from Florida State University, His MS in Computer Science - Artificial Intelligence from Isfahan University of Technology and his B.S. in Computer Science at Iran University of Science and Technology. He also worked as a post doctoral research associate at University of Wisconsin-Madison before joining MSKCC.

IntroductionIt is a well-established fact that your machine-learning model is only as good as the data it is fed. ML model trained on bad-quality data usually has a number of issues. Here are a few ways that bad data might affect machine-learning models -1. Predictions that are wrong may be made as a result of errors, missing numbers, or other irregularities in low-quality data. The model's predictions are likely to be inaccurate if the data used to train is unreliable.2. Bad data can also bias the model. The ML model can learn and reinforce these biases if the data is not representative of the real-world situations, which can result in predictions that are discriminating.3. Poor data also disables the the ability of ML model to generalize on fresh data. Poor data may not effectively depict the underlying patterns and relationships in the data.4. Models trained on bad-quality data might need more retraining and maintenance. The overall cost and complexity of model deployment could rise as a result.As a result, it is critical to devote time and effort to data preprocessing and cleaning in order to decrease the impact of bad data on ML models. Furthermore, to ensure the model's dependability and performance, it is often necessary to use domain knowledge to recognize and address data quality issues.It might come as a surprise, but gold-standard datasets like ImageNet, CIFAR, MNIST, 20News, and more also contain labeling issues. I have put in some examples below for reference -The above snippet is from the Amazon sentiment review dataset , where the original label was Neutral in both cases, whereas Cleanlab and Mechanical turk said it to be positive (which is correct).The above snippet is from the MNIST dataset, where the original label was marked to be 8 and 0 respectively, which is incorrect. Instead, both Cleanlab and Mechanical Turk said it to be 9 and 6 (which is correct).Feel free to check out labelerrors to explore more such cases in similar datasets.Introducing CleanlabThis is where Cleanlab can come in handy as your best bet. It helps by automatically identifying problems in your ML dataset, it assists you in cleaning both data and labels. This data centric AI software uses your existing models to estimate dataset problems that can be fixed to train even better models. The graphic below depicts the typical data-centric AI model development cycle:Apart from the standard way of coding all the way through finding data issues, it also offers Cleanlab Studio - a no-code platform for fixing all your data errors. For the purpose of this blog, we will go the former way on our sample use case.Getting Hands-on with CleanlabInstallationInstalling cleanlab is as easy as doing a pip install. I recommend installing optional dependencies as well, you never know what you need and when. I also installed sentence transformers, as I would be using them for vectorizing the text. Sentence transformers come with a bag of many amazing models, we particularly use ‘all-mpnet-base-v2’ as our choice of sentence-transformers for vectorizing text sequences. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search. Feel free to check out this for the list of all models and their comparisons.pip install ‘cleanlab[all]’ pip install sentence-transformersDatasetWe picked the SMS Spam Detection dataset as our choice of dataset for doing the experimentation. It is a public set of labeled SMS messages that have been collected for mobile phone spam research with total instances of roughly ~5.5k. The below graphic gives a sneak peek of some of the samples from the dataset.Data PreviewCodeLet’s now delve into the code. For demonstration purposes, we inject a 5% noise in the dataset, and see if we are able to detect them and eventually train a better model.Note: I have also annotated every segment of the code wherever necessary for better understanding.import pandas as pd from sklearn.model_selection import train_test_split, cross_val_predict       from sklearn.preprocessing import LabelEncoder               from sklearn.linear_model import LogisticRegression       from sentence_transformers import SentenceTransformer       from cleanlab.classification import CleanLearning       from sklearn.metrics import f1_score # Reading and renaming data. Here we set sep=’\t’ because the data is tab       separated.       data = pd.read_csv('SMSSpamCollection', sep='\t')       data.rename({0:'label', 1:'text'}, inplace=True, axis=1)       # Dropping any instance of duplicates that could exist       data.drop_duplicates(subset=['text'], keep=False, inplace=True)       # Original data distribution for spam and not spam (ham) categories       print (data['label'].value_counts(normalize=True))       ham 0.865937       spam 0.134063       # Adding noise. Switching 5% of ham data to ‘spam’ label       tmp_df = data[data['label']=='ham']               examples_to_change = int(tmp_df.shape[0]*0.05)       print (f'Changing examples: {examples_to_change}')       examples_text_to_change = tmp_df.head(examples_to_change)['text'].tolist() changed_df = pd.DataFrame([[i, 'spam'] for i in examples_text_to_change])       changed_df.rename({0:'text', 1:'label'}, axis=1, inplace=True)       left_data = data[~data['text'].isin(examples_text_to_change)]       final_df = pd.concat([left_data, changed_df])               final_df.reset_index(drop=True, inplace=True)       Changing examples: 216       # Modified data distribution for spam and not spam (ham) categories       print (final_df['label'].value_counts(normalize=True))       ham 0.840016       spam 0.159984    raw_texts, raw_labels = final_df["text"].values, final_df["label"].values # Converting label into integers encoder = LabelEncoder() encoder.fit(raw_train_labels)       train_labels = encoder.transform(raw_train_labels)       test_labels = encoder.transform(raw_test_labels)       # Vectorizing text sequence using sentence-transformers transformer = SentenceTransformer('all-mpnet-base-v2') train_texts = transformer.encode(raw_train_texts)       test_texts = transformer.encode(raw_test_texts)       # Instatiating model instance model = LogisticRegression(max_iter=200) # Wrapping the sckit model around CL cl = CleanLearning(model) # Finding label issues in the train set label_issues = cl.find_label_issues(X=train_texts, labels=train_labels) # Picking top 50 samples based on confidence scores identified_issues = label_issues[label_issues["is_label_issue"] == True] lowest_quality_labels =       label_issues["label_quality"].argsort()[:50].to_numpy()       # Beauty print the label issue detected by CleanLab def print_as_df(index):    return pd.DataFrame(              {    "text": raw_train_texts,              "given_label": raw_train_labels,           "predicted_label": encoder.inverse_transform(label_issues["predicted_label"]),       },       ).iloc[index]       print_as_df(lowest_quality_labels[:5]) As we can see, Cleanlab assisted us in automatically removing the incorrect labels and training a better model with the same parameters and settings. In my experience, people frequently ignore data concerns in favor of building more sophisticated models to increase accuracy numbers. Improving data, on the other hand, is a pretty simple performance win. And, thanks to products like Cleanlab, it's become really simple and convenient.Feel free to access and play around with the above code in the Colab notebook hereConclusionIn conclusion, Cleanlab offers a straightforward solution to enhance data quality by addressing label inconsistencies, a crucial step in building more reliable and accurate machine learning models. By focusing on data integrity, Cleanlab simplifies the path to better performance and underscores the significance of clean data in the ever-evolving landscape of AI. Elevate your model's accuracy by investing in data quality, and explore the provided code to see the impact for yourself.Author BioPrakhar has a Master’s in Data Science with over 4 years of experience in industry across various sectors like Retail, Healthcare, Consumer Analytics, etc. His research interests include Natural Language Understanding and generation, and has published multiple research papers in reputed international publications in the relevant domain. Feel free to reach out to him on LinkedIn

Why now is the time to start your automating journey with Intune and GraphWith more and more organizations moving to Microsoft Intune and IT departments under constant strain, automating your regular tasks is an excellent way to free up time to concentrate on your many other important tasks.When dealing with Microsoft Intune and the related Microsoft Entra tasks, everything clicked within the portal UI is sending API requests to Microsoft Graph which sits underneath everything and controls exactly what is happening and where.Fortunately, Microsoft Graph is a public API, therefore anything being carried out within the portal, can be scripted and automated.Imagine a world where by using automation, you log in to your machine in the morning, and there waiting for you is an email, or Teams message that ran overnight containing everything you need to know about your environment.  You can take this information to quickly resolve any new issues and be extra proactive with your user base, calling them to resolve issues before they have even noticed themselves.This is just the tip of the iceberg of what can be done with Microsoft Graph, the possibilities are endless.Microsoft Graph is a web API that can be accessed and manipulated via most programming and scripting languages, so if you have a preferred language, you can get started extremely quickly.  For those starting out, PowerShell is an excellent choice as the Microsoft Graph SDK includes modules that take the effort out of the initial connection and help write the requests.For those more experienced, switching to the C# SDK opens up more scalability and quicker performance, but ultimately it is the same web requests underneath so once you have the basic knowledge of the API, moving these skills between languages is much easier.When looking to learn the API, an excellent starting point is to use the F12 browser tools and select Network.  Then click around in the portal and have a look at the network traffic.This will be in the form of GET, POST, PUT, DELETE, and BATCH requests depending on what action is being performed.  Get is used to retrieve information and is one-way traffic, retrieving from Graph and returning to the client.POST and PUT are used to send data to Graph.DELETE is fairly self-explanatory and is used to delete records.BATCH is used to increase performance in more complex tasks.  This groups multiple Graph API calls into one command which reduces the calls and improves the performance.  It works extremely well, but starting with the more basic commands is always recommended.Once you have mastered Graph calls from a local device with interactive authentication, the next step is to create Entra App Registrations and run locally, but with non-interactive authentication.This will feed into true automation where tasks can be set to run without any user involvement, at this point learning about Azure Automation accounts and Azure Function and Logic Apps will prove incredibly useful.For larger environments, you can take it a step further and use Azure DevOps pipelines to trigger tasks and even implement approval processes.Some real-world examples of automation with Graph include new environment configuration, policy management, and application management, right through to documenting and monitoring policies. Once you have the basic knowledge of Graph API and PowerShell, it is simply a case of slotting them together and watching where the process takes you.  The learning never stops, before you know it you will be creating tools for your other IT staff to use to quickly retrieve passwords on the go, or do standard tasks without needing elevated privileges.Now, I know what you are thinking, this all sounds fantastic and exactly what I need, but how do I get started and how do I find the time to learn a new skill like this?We can start with time management.  I am sure throughout your career you have had to learn new software, systems, and technologies without any formal training and the best way to do that is by learning by doing.  The same can apply here, when you are completing your normal tasks, simply have the F12 network tools open and have a quick look at the URLs and requests being sent.If you can, try and find a few minutes per day to do so some practice scripts, ideally in a development environment, but if not, start with GET requests which cannot do any damage.To take it further and learn more about PowerShell, Graph, and Intune, check out my “Microsoft Intune Cookbook” which runs through creating a tenant from scratch, both in the portal and via Graph, including code samples for everything possible within the Intune portal.  You can use these samples to expand upon and meet your needs while learning about both Intune and Graph.Author BioAndrew Taylor is an End-User Compute architect with 20 years IT experience across industries and a particular interest in Microsoft Cloud technologies, PowerShell and Microsoft Graph. Andrew graduated with a degree in Business Studies in 2004 from Lancaster University and since then has obtained numerous Microsoft certifications including Microsoft 365 Enterprise Administrator Expert, Azure Solutions Architect Expert and Cybersecurity Architect Expert amongst others. He currently working as an EUC Architect for an IT Company in the United Kingdom, planning and automating the products across the EUC space. Andrew lives on the coast in the North East of England with his wife and two daughters.

IntroductionIn the wild world of videogames, you'll inevitably encounter a foe that needs to be both engaging and captivating. This opponent isn't just a bunch of nice-to-see polygons and textures; it needs to be a challenge that'll keep your players hooked to the screen.Let's be honest, as a game developer, crafting a truly engaging opponent is often a challenge that rivals the one your players will face!In video games, we often use the term Artificial Intelligence (AI) to describe characters that are not controlled by the player, whether they are enemies or friendly entities. There are countless ways to develop compelling characters in video games. In this article, we'll explore one specific solution offered by Unreal Engine: behavior trees.NoteCitations come from my Artificial Intelligence in Unreal Engine 5 book.Using the Unreal Shooting Gym ProjectFor this article, I have created a dedicated project called Unreal Shooting Gym. You can freely download it from GitHub: https://github.com/marcosecchi/unreal-shooting-gym and open it up with Unreal Engine 5.4.Once opened, you should see a level showing a lab with a set of targets and a small robot armed with a gun (A.K.A. RoboGun), as shown in Figure 1: Figure 1. The project level.If you hit the Play button, you should notice the RoboGun rotating toward a target while shooting. Once the target has been hit, the RoboGun will start rotating towards another one. All this logic has been achieved through a behavior tree, so let’s see what it is all about.Behavior Trees“In the universe of game development, behavior trees are hierarchical structures that govern the decision-making processes of AI characters, determining their actions and reactions during gameplay.”Unreal Engine offers a solid framework for handling behavior trees based on two main elements: the blackboard and behavior tree assets.Blackboard Asset“In Unreal Engine, the Blackboard [...] acts as a memory space – some sort of brain – where AI agents can read and write data during their decision-making process.“By opening the AI project folder, you can double-click the BB_Robogun asset to open it. You will be presented with the blackboard that, as you can see from Figure 2, is quite simple to understand. Figure 2. The AI blackboardAs you can see there’s a couple of variables – called keys – that are used to store a reference to the actor owning the behavior tree – in this case, the RoboGun – and to the target object that will be used to rotate the RoboGun.Behavior Tree Asset“In Unreal Engine, behavior trees are assets that are edited in a similar way to Blueprints – that is, visually – by adding and linking a set of nodes with specific functionalities to form a behavior tree graph.”Now, double-click the BT_RoboGun asset located in the AI folder to open the behavior tree. You should see the tree structure depicted in Figure 3:Figure 3. The AI behavior treeAlthough this is a pretty simple behavior logic, there’s a lot of things involved here. First of all, you will notice that there is a Root node; this is where the behavior logic starts from.After that, you will see that there are three gray-colored nodes; these are defined composite nodes.“Composite nodes define the root of a branch and set the rules for its execution.”Each of them behaves differently, but it is sufficient to say that they control the subtree that will be executed; as an example, the Shoot Sequence node will execute all the subtrees one after the other.The purple-colored nodes are called tasks and they are basically the leaves of the tree, whose aim is to execute actions. Unreal Engine comes with some predefined tasks, but you will be able to create your own through Blueprints or C++.As an example, consider the Shoot task depicted in Figure 4: Figure 4. The Shoot task In this Blueprint, when the task is executed, it will call the Shoot method – by means of a ShootInterface – and then end the execution with success. For a slightly more complex task, please check the  BTTask_SeekTarget asset.Get back to the behavior tree, and you will notice that the Find Random Target node has a blue-colored section called Is Target Set? This is a decorator. “Decorators provide a way to add additional functionality or conditions to the execution of a portion of a behavior tree.”In our case, the decorator is checking if the TargetActor blackboard key is not set; the corresponding task will be executed only if that key is not set – that is, we have no viable target. If the target is set, this decorator will block task execution and the parent selector node – the Root Selector node – will execute the next subtree.Environment QueriesUnreal Engine provides an Environment Query System (EQS) framework that allows data collection about the virtual environment. AI agents will be able to make informed decisions based on the results.In our behavior tree, we are running an environment query to find a viable target in the Find Random Target task. The query I have created – called EQ_FindTarget – is pretty simple as it just queries the environment looking for instances of the class BP_Target, as shown in Figure 5:Figure 5. The environment queryPawn and ControllerOnce you have created your behavior tree, you will need to execute it through an AIController, the class that is used to possess pawns or characters in order to make them proper AI agents. In the Blueprints folder, you can double-click on the RoboGunController asset to check the pretty self-explanatory code depicted in Figure 6:Figure 6. The character controller codeAs you can see, it’s just a matter of running a behavior tree asset. Easy, isn’t it?If you open the BP_RoboGun asset, you will notice that, in the Details panel, I have set the AI Controller Class to the RoboGunController; this will make the RoboGun pawn be automatically possessed by the RoboGunController.ConclusionThis concludes this brief overview of the behavior tree system; I encourage you to explore the possibilities and more advanced features – such as writing your code the C++ way – by reading my new book “Artificial Intelligence in Unreal Engine 5”; I promise you it will be an informative and, sometimes, funny journey!Author BioMarco Secchi is a freelance game programmer who graduated in Computer Engineering at the Polytechnic University of Milan. He is currently lecturer of the BA in Creative Technologies and of the MA in Creative Media Production. He also mentors BA students in their final thesis projects. In his spare time, he reads (a lot), plays (less than he would like) and practices (to some extent) Crossfit.

Introduction The world of software development is constantly evolving, and one of the most significant shifts in recent years has been the move from monolithic architectures to microservices. In his book "Building Microservices with Node.js: Explore Microservices Applications and Migrate from a Monolith Architecture to Microservices," Daniel Kapexhiu offers a comprehensive guide for developers who wish to understand and implement microservices using Node.js. This article delves into the book's key themes, including an analysis of Node.js as a technology, best practices for JavaScript in microservices, and the unique insights that Kapexhiu brings to the table. Node.js: The Backbone of Modern Microservices Node.js has gained immense popularity as a runtime environment for building scalable network applications, particularly in the realm of microservices. It is built on Chrome's V8 JavaScript engine and uses an event-driven, non-blocking I/O model, which makes it lightweight and efficient. These characteristics are essential when dealing with microservices, where performance and scalability are paramount. The author effectively highlights why Node.js is particularly suited for microservices architecture. First, its asynchronous nature allows microservices to handle multiple requests concurrently without being bogged down by long-running processes. This is crucial in a microservices environment where each service should be independently scalable and capable of handling a high load. Moreover, Node.js has a vast ecosystem of libraries and frameworks, such as Express.js and Koa.js, which simplifies the development of microservices. These tools provide a solid foundation for building RESTful APIs, which are often the backbone of microservices communication. The author emphasizes the importance of choosing the right tools within the Node.js ecosystem to ensure that microservices are not only performant but also maintainable and scalable. Best Practices for JavaScript in Microservices While Node.js provides a robust platform for building microservices, the importance of adhering to JavaScript best practices cannot be overstated. In his book, the author provides a thorough analysis of the best practices for JavaScript when working within a microservices architecture. These best practices are designed to ensure code quality, maintainability, and scalability. One of the core principles the author advocates is the use of modularity in code. JavaScript’s flexible and dynamic nature allows developers to break down applications into smaller, reusable modules. This modular approach aligns perfectly with the microservices architecture, where each service is a distinct, self-contained module. By adhering to this principle, developers can create microservices that are easier to maintain and evolve over time. The author also stresses the importance of following standard coding conventions and patterns. This includes using ES6/ES7 features such as arrow functions, destructuring, and async/await, which not only make the code more concise and readable but also improve its performance. Additionally, he underscores the need for rigorous testing, including unit tests, integration tests, and end-to-end tests, to ensure that each microservice behaves as expected. Another crucial aspect this book covers is error handling. In a microservices architecture, where multiple services interact with each other, robust error handling is essential to prevent cascading failures. The book provides practical examples of how to implement effective error-handling mechanisms in Node.js, ensuring that services can fail gracefully and recover quickly. Problem-Solving with Microservices Transitioning from a monolithic architecture to microservices is not without its challenges. The author does not shy away from discussing the potential pitfalls and complexities that developers might encounter during this transition. He offers practical advice on how to decompose a monolithic application into microservices, focusing on identifying the right boundaries between services and ensuring that they communicate efficiently. One of the key challenges in a microservices architecture is managing data consistency across services. The author addresses this issue by discussing different strategies for managing distributed data, such as event sourcing and the use of a centralized message broker. He provides examples of how to implement these strategies using Node.js, highlighting the trade-offs involved in each approach. Another common problem in microservices is handling cross-cutting concerns such as authentication, logging, and monitoring. The author suggests solutions that involve leveraging middleware and service mesh technologies to manage these concerns without introducing tight coupling between services. This allows developers to maintain the independence of each microservice while still addressing the broader needs of the application. Unique Insights and Experiences What sets this book apart is the depth of practical insights and real-world experiences that he shares. This book goes beyond the theoretical aspects of microservices and Node.js to provide concrete examples and case studies from his own experiences in the field. These insights are invaluable for developers who are embarking on their microservices journey. For instance, the author discusses the importance of cultural and organizational changes when adopting microservices. He explains how the shift to microservices often requires changes in team structure, development processes, and even the way developers think about code. By sharing his experiences with these challenges, the author helps readers anticipate and navigate the broader implications of adopting microservices. Moreover, the author offers guidance on the operational aspects of microservices, such as deploying, monitoring, and scaling microservices in production. He emphasizes the need for automation and continuous integration/continuous deployment (CI/CD) pipelines to manage the complexity of deploying multiple microservices. His advice is grounded in real-world scenarios, making it highly actionable for developers. Conclusion "Building Microservices with Node.js: Explore Microservices Applications and Migrate from a Monolith Architecture to Microservices" by Daniel Kapexhiu is an essential read for any developer looking to understand and implement microservices using Node.js. The book offers a comprehensive guide that covers both the technical and operational aspects of microservices, with a strong emphasis on best practices and real-world problem-solving. The author’s deep understanding of Node.js as a technology, combined with his practical insights and experiences, makes this book a valuable resource for anyone looking to build scalable, maintainable, and efficient microservices. Whether you are just starting your journey into microservices or are looking to refine your existing microservices architecture, this book provides the knowledge and tools you need to succeed. Author BioDaniel Kapexhiu is a software developer with over 6 years of working experience developing web applications using the latest technologies in frontend and backend development. Daniel has been studying and learning software development for about 12 years and has extended expertise in programming. He specializes in the JavaScript ecosystem, and is always updated about new releases of ECMAScript. He is ever eager to learn and master the new tools and paradigms of JavaScript.

Introduction Are you an Excel user looking to push your data analysis capabilities beyond the familiar cells and formulas? If so, you're about to embark on a transformative journey. With the integration of R and Python, you can elevate Excel into a powerhouse of advanced data analysis and visualization. In this blog post, inspired by the book "Extending Excel with Python and R," co-authored by myself and David Kun, we will dive deep into practical implementation, focusing on how to automate data visualization in Excel using these powerful programming languages. Practical Implementation: Creating Advanced Data Visualizations In the world of data analysis, visual representation is key to understanding complex datasets. Excel, while equipped with basic charting tools, often requires enhancement for more sophisticated visuals. By integrating R and Python, you can create dynamic and detailed graphs that bring your data to life. Task: Automating Data Visualization with Python and R Step-by-Step Guide Step 1: Set Up Your Environment Before jumping into visualization, ensure you have the necessary tools installed. You will need: Excel: Ensure you have a version that supports VBA (Visual Basic for Applications). Python: Install Python on your computer. You can download it from the official Python website. R: Similarly, install R from the Comprehensive R Archive Network (CRAN). Libraries: For Python, install `pandas`, `matplotlib`, and `openpyxl` using pip. For R, install `ggplot2` and `readxl`.  Step 2: Importing Data Begin by importing your Excel data into Python or R. Here’s a Python snippet using pandas:  In R, use readxl:  Step 3: Creating Visualizations Python Example Using Matplotlib, you can create a simple line plot: Python Example   R Example With ggplot2, the process is equally straightforward where df is some data frame loaded in:  Step 4: Integrating Visualizations into Excel Once your visualization is created, the next step is to integrate it back into Excel. This can be done manually, or you can automate it using VBA or an API endpoint. Python Integration Using openpyxl, you can embed images:   R Integration For R, you might automate this process using R scripts that interact with Excel via VBA or other packages like `officer`.  Step 5: Automating the Entire Workflow To automate, consider using Python scripts executed from Excel VBA or R scripts called through Excel's RExcel plugin. This way, you can refresh data and update visualizations with minimal effort. Conclusion By integrating R and Python with Excel, you unlock a realm of possibilities for data visualization and analysis, turning Excel from a simple spreadsheet tool into a comprehensive data analytics suite. This guide provides a snapshot of what you can achieve, and with further exploration, the potential is limitless. Author Bio Steven Sanderson is a Manager of Applications with a deep passion for data and its compliments: cleaning, analysis, visualization and communication. He is known primarily for his work in R. After his MPH, Steven continued his work in the healthcare industry as a clinical decision support analyst working his way up to Manager of Applications at Stony Brook Medicine for Patient Financial Services. He currently is focused on expanding functions in his healthyverse suite of packages while also slimming them down and expanding their robustness. He also now enjoys helping mentor junior employees to set them up for success. David Kun is a mathematician and actuary who has always worked in the gray zone between quantitative teams and ICT, aiming to build a bridge. He is a co-founder and director of Functional Analytics, the creator of the ownR infinity platform. As a data scientist, he also uses ownR for his daily work. His projects include time series analysis for demand forecasting, computer vision for design automation, and visualization. Looking to Master Excel with Python and R?If you're excited about extending Excel’s capabilities with powerful tools like Python and R, Extending Excel with Python and R, authored by Steven Sanderson, David Kun, offers an in-depth guide to seamlessly integrating these languages into your Excel workflow. It covers everything from automating data tasks to advanced visualizations, all tailored for Excel enthusiasts.

The Packt library provides you with all the tools you need to stay relevant in tech, whether you’re looking to brush up your PHP skills or take advantage of our learning paths to start from scratch. Here’s our top five reasons to begin a Packt subscription.

IntroductionIn today’s rapidly shifting world of design and trends, artificial intelligence (AI) has become a reality! It’s now a creative partner that helps designers and creative minds go further and stand out from the competition. One of the leading AI tools revolutionizing the design process is Midjourney. Whether you’re an experienced professional or a curious beginner, mastering this tool can enhance your creative workflow and open up new possibilities for branding, advertising, and personal projects. In this article, we’ll explore how AI can act as a brainstorming partner, help overcome creative blocks, and provide insights into best practices for unlocking its full potential. Using AI as my creative colleague AI tools like Midjourney have the potential to become more than just assistants; they can function as creative collaborators. Often, as designers, we hit roadblocks—times when ideas run dry, or creative fatigue sets in. This is where Midjourney steps in, acting as a colleague who is always available for brainstorming. By generating multiple variations of an idea, it can inspire new directions or unlock solutions that may not have been immediately apparent. The beauty of AI lies in its ability to combine data insights with creative freedom. Midjourney, for instance, uses text prompts to generate visuals that help spark creativity. Whether you’re building moodboards, conceptualizing ad campaigns, or creating a specific portfolio of images, the tool’s vast generative capabilities enable you to break free from mental blocks and jumpstart new ideas. Best practices and trends in AI for creative workflows While AI offers incredible creative opportunities, mastering tools like Midjourney requires understanding its potential and limits. A key practice for success with AI is knowing how to use prompts effectively. Midjourney allows users to guide the AI with text descriptions or just image input, and the more you fine-tune those prompts, the closer the output aligns with your vision. Understanding the nuances of these prompts—from image weights to blending modes—enables you to achieve optimal results. A significant trend in AI design is the combination of multiple tools. MidJourney is powerful, but it’s not a one-stop solution. The best results often come from integrating other third-party tools like Kling.ai or Gen 3 Runway. These complementary tools help refine the output, bringing it to a professional level. For instance, Midjourney might generate the base image, but tools like Kling.ai could animate that image, creating dynamic visuals perfect for social media or advertising. Additionally, staying up to date with AI updates and model improvements is crucial. Midjourney regularly releases new versions that bring refined features and enhancements. Learning how these updates impact your workflow is a valuable skill, as mastering earlier versions helps build a deeper understanding of the tool’s evolution and future potential. The book, The Midjourney Expedition, dives into these aspects, offering both beginners and advanced users a guide to mastering each version of the tool. Overcoming creative blocks and boosting productivity One of the most exciting aspects of using AI in design is its ability to alleviate creative fatigue. When you’ve been working on a project for hours or days, it’s easy to feel stuck. Here’s an example of how AI helped me when I needed to create a mockup for a client’s campaign. I wasn’t finding suitable mockups on regular stock photo sites, so I decided to create my own.  I went to the MidJourney website: www.midjourney.com  Logged in using my Discord or Google account.  Go to Create (step 1 in the image below), enter the prompt (3D rendering of a blank vertical lightbox in front of a wall of a modern building. Outdoor advertising mockup template, front view) in the text box ( step 2), click on the icon on the right (step 3) to open the settings box (step 4) change any settings you want. In this case, lets keep it with the default settings, I just adjusted the settings to make the image landscape-oriented and pressed enter on my keyboard. 4 images will appear, choose the one you like the most or rerun the job, until you fell happy with the result.  I got my image, but now I need to add the advertisement I had previously generated on Midjourney, so I can present to my client some ideas for the final mockup. Lets click on the image to enlarge it and get more options. On the bottom of the page lets click on Editor In Editor mode and with the erase tool selected, erase the inside of the billboard frame, next copy the URL of the image you want to use as a reference to be inserted in the billboard, and edit your prompt to: https://cdn.midjourney.com/urloftheimage.png  3D rendering of a, Fashion cover of "VOGUE" magazine, a beautiful girl in a yellow coat and sunglasses against a blue background inside the frame, vertical digital billboard mockup in front of a modern building with a white wall at night. Glowing light inside the frame., in high resolution and high quality. And press Submit.  This is the final result.  In case you master any editing tool, you can skip this last step and personalize the mockup, for instance, in Photoshop. This is just one example of how AI saved me time and allowed me to create a custom mockup for my client. For many designers, MidJourney serves as another creative tool, always fresh with new perspectives, and helping unlock ideas we hadn’t considered. Moreover, AI can save hours of work. It allows designers to skip repetitive tasks, such as creating multiple iterations of mockups or ad layouts. By automating these processes, creatives can focus on refining their work and ensuring that the main visual content serves a purpose beyond aesthetics. The challenges of writing about a rapidly evolving tool Writing The Midjourney Expedition was a unique challenge because I was documenting a technology that evolves daily. AI design tools like Midjourney are constantly being updated, with new versions offering improved features and refined models. As I wrote the book, I found myself not only learning about the tool but also integrating the latest advancements as they occurred. One of the most interesting parts was revisiting the older versions of MidJourney. These models, once groundbreaking, now seem like relics, yet they offer valuable insights into how far the technology has come. Writing about these early versions gave me a sense of nostalgia, but it also highlighted the rapid progress in AI. The same principles that amazed us two years ago have been drastically improved, allowing us to create more accurate and visually stunning images. The book is not just about creating beautiful images, it’s about practical applications. As a communication designer, I’ve always focused on using AI to solve real-world problems, whether for branding, advertising, or storytelling. And I find Midjourney to be a powerful solution for any creative who need to go one step further in a effective way. Conclusion AI is not the future of design, it’s already here! While I don’t believe AI will replace creatives, any creator who masters these tools may replace those who don’t use them. Tools like Midjourney are transforming how we approach creative workflows and even final outcomes, enabling designers to collaborate with AI, overcome creative blocks, and produce better results faster. Whether you're new to AI or an experienced user, mastering these tools can unlock new opportunities for both personal and professional projects. By combining Midjourney with other creative tools, you can push your designs further, ensuring that AI serves as a valuable resource for your creative tasks. Unlock the full potential of AI in your creative workflows with "The Midjourney Expedition". This book is for creative professionals looking to leverage Midjourney. You’ll learn how to produce stunning AI art, streamline your creative process, and incorporate AI into your work, all while gaining a competitive edge in your industry.Author BioMargarida Barreto is a seasoned communication designer with over 20 years of experience in the industry. As the author of The Midjourney Expedition, she empowers creatives to explore the full potential of AI in their workflows. Margarida specializes in integrating AI tools like Midjourney into branding, advertising, and design, helping professionals overcome creative challenges and achieve outstanding results.