What this book covers
Chapter 1, Machine Learning and Machine Learning Solutions Architecture, introduces the core concepts of ML and the ML solutions architecture function.
Chapter 2, Business Use Cases for Machine Learning, talks about the core business fundamentals, workflows, and common ML use cases in financial services, media entertainment, health care, manufacturing, and retail.
Chapter 3, Machine Learning Algorithms, introduces common ML and deep learning algorithms for classification, regression, clustering, time series, recommendations, computer vision, natural language processing, and data generation. You will get hands-on experience of setting up a Jupyter server and building ML models on your local machine.
Chapter 4, Data Management for Machine Learning, covers platform capabilities, system architecture, and AWS tools for building data management capabilities for ML. You will develop hands-on skills with AWS services for building data management pipelines for ML.
Chapter 5, Open Source Machine Learning Libraries, covers the core features of scikit-learn, Spark ML, and TensorFlow, and how to use these ML libraries for data preparation, model training, and model serving. You will practice building deep learning models using TensorFlow and PyTorch.
Chapter 6, Kubernetes Container Orchestration Infrastructure Management, introduces containers, Kubernetes concepts, Kubernetes networking, and Kubernetes security. Kubernetes is a core open source infrastructure for building open source ML solutions. You will also practice setting up the Kubernetes platform on AWS EKS and deploying an ML workload in Kubernetes.
Chapter 7, Open Source Machine Learning Platform, talks about the core concepts and the technical details of various open source ML platform technologies, such as Kubeflow, MLflow, AirFlow, and Seldon Core. The chapter also covers how to use these technologies to build a data science environment and ML automation pipeline, and provides you with instructions to develop hands-on experience with these open source technologies.
Chapter 8, Building a Data Science Environment Using AWS Services, introduces various AWS managed services for building data science environments, including Amazon SageMaker, Amazon ECR, and Amazon CodeCommit. You will also get hands-on experience with these services to configure a data science environment for experimentation and model training.
Chapter 9, Building an Enterprise ML Architecture with AWS ML Services, talks about the core requirements for an enterprise ML platform, discusses the architecture patterns for building an enterprise ML platform on AWS, and dives deep into the various core ML capabilities of SageMaker and other AWS services. You will also learn MLOps and monitoring architecture with a hands-on exercise using sample ML pipelines for model training and model deployment.
Chapter 10, Advanced ML Engineering, covers core concepts and technologies for large-scale distributed model training, such as data parallel and model parallel model training using DeepSpeed and PyTorch DistributeDataParallel. It also dives deep into the technical approaches for low-latency model inference, such as using hardware acceleration, model optimization, and graph and operator optimization. You will also get hands on with distributed data parallel models training using a SageMaker training cluster.
Chapter 11, ML Governance, Bias, Explainability, and Privacy, discusses the ML governance, bias, explainability, and privacy requirements and capabilities for production model deployment. You will also learn techniques for bias detection, explainability, and ML privacy with hands-on exercises using SageMaker Clarify and PyTorch Opacus.
Chapter 12, Building ML Solutions with AWS AI Services, introduces AWS AI services and architecture patterns for incorporating these AI services into ML-powered business applications.
