Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Faisal Masood

Brigoli

£16.99 per month

4.1 (10 Ratings)

Paperback Jun 2022 384 pages 1st Edition

Faisal Masood

Brigoli

£16.99 per month

4.1 (10 Ratings)

Paperback Jun 2022 384 pages 1st Edition

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Key benefits

Build a complete machine learning platform on Kubernetes
Improve the agility and velocity of your team by adopting the self-service capabilities of the platform
Reduce time-to-market by automating data pipelines and model training and deployment

Description

MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.

Who is this book for?

This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.

What you will learn

Understand the different stages of a machine learning project
Use open source software to build a machine learning platform on Kubernetes
Implement a complete ML project using the machine learning platform presented in this book
Improve on your organization s collaborative journey toward machine learning
Discover how to use the platform as a data engineer, ML engineer, or data scientist
Find out how to apply machine learning to solve real business problems

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Amazon verified reviews

aly Sep 21, 2022

I am coming from a strong k8s background and I wanted to transfer and bridge this knowledge with ML. This book gives a very practical guide to fill in the gaps and give me the needed MLOps practical and theoretical knowledge to use k8s for ML deployments on production.I highly recommend it for readers with k8s background and also who want to learn the modern way to deploy and build ML models in the public cloud such as AWS.

Amazon Verified review

Daniel Sullivan Sep 12, 2022

If you are building and deploying ML models in Kubernetes, this is a great place to start. The book does a good job of providing an overview of MLOps as data engineering while also introducing ML concepts like feature engineering and model evaluation. These provide the foundation for detailed explanations of how to install and use ML platform components, like Jupyter Notebooks. Apache Airflow, MLFlow, and Spark. I especially like the details on setting up Keycloak for authentication, which isn't strictly an ML component but the lack of an authentication system can be a blocker to production deployments.The book covers a lot of ground but it is well organized and the extensive use of screen shots and diagrams complement the text.

Guangping zhang Jul 28, 2022

This book is about machine learning on KubernetesThe book contain a lots of contents about advanced progress on machine learning:MLOps is the convergence of ML, DevOps, and data engineering disciplines that focus on running ML in production.Kubernetes, Kubernetes-based software to run anywhere, from small on-premises data centers to large cloud platforms AWS, GCP and Azure.This capability will give you the portability to run your ML platform anywhere you want.Kubeflow is a machine learning toolkit that provides a pipeline solution called Kubeflow PipelinesThis book discuss both the Data engineering pipeline and ML engineering pipeline, introduce Using MLFlow as an experiment tracking system, Using MLFlow as a model registry systemAutomation is the most popular field now, the model deployment and monitoring need to be automated to increase the efficiency.The GKE platform can package, deploy and automate the model onto the platform, so You can automate all these steps using the workflow engine provided by the platform.GKE also can monitor your model performance and decide whether the model needs retraining on the new dataset not.This book also introduces Docker and containers very well.Recently GKE was updated to Vertex AI, unfortunately this book doesn't mention this update, but the reader can easily find the knowledge from online.I strongly suggest you read this good book.

venky Aug 02, 2022

Best value for price and great content. I really enjoyed reading and implementing concepts explained in the book. Most books introduce topics as bits and pieces but miss composing an end to end project with topics they targeted to elaborate, Faisal and Ross did great job in crafting chapters from novice to advanced level and curate the exercises across. I can say that even if you are an experienced engineer in cloud tech with ML area, you will learn good amount of stuff from this book. This book has sparked my interest to learn more about Kubernetes and building an end to end platform, I am gonna publish a blog on ML platform setup. Thanks to Authors for pouring their experience and crafting such good hands-on exercises with great detail.

Yiqiao Yin Aug 03, 2022

Both authors come from Red Hat and provided a lot of industry experience. I really like the book from the beginning because it starts with motivation and challenges in the industry. I really enjoyed the cloud-agnostic property. It's interesting to see from the author's perspectives how the proposed platform can be contributing factor to the entire MLOps and also deployable from cloud-based platforms. I find this part well written and I have had a lot of fun reading these chapters. Coming from my jobs working with people from different parts of the world, I think it's important that today's software platform is equipped with cloud-based technology so it resonate a lot with me to see that this is covered in the book. I have had a lot of fun and joy reading this book and I highly recommend it to everybody else! Please review the video if you want an in-depth feedback of this book!

Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes

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