Learn Amazon SageMaker

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor

▶What You Will Learn
⦁Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS)
⦁Become well-versed with data annotation and preparation techniques
⦁Use AutoML features to build and train machine learning models with AutoPilot
⦁Create models using built-in algorithms and frameworks and your own code
⦁Train computer vision and NLP models using real-world examples
⦁Cover training techniques for scaling, model optimization, model debugging, and cost optimization
⦁Automate deployment tasks in a variety of configurations using SDK and several automation tools

▶Key Features
⦁Build, train, and deploy machine learning models quickly using Amazon SageMaker
⦁Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques
⦁Improve productivity by training and fine-tuning machine learning models in production

▶Who This Book Is For
This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

▶What this book covers
⦁ Chapter 1, Getting Started with Amazon SageMaker, provides an overview of Amazon SageMaker, what its capabilities are, and how it helps solve many pain points faced by ML projects today.

⦁ Chapter 2, Handling Data Preparation Techniques, discusses data preparation options. Although this it isn't the core subject of the book, data preparation is a key topic in ML, and it should be covered at a high level.

⦁ Chapter 3, AutoML with Amazon SageMaker AutoPilot, shows you how to build, train, and optimize ML models automatically with Amazon SageMaker AutoPilot.

⦁ Chapter 4, Training Machine Learning Models, shows you how to build and train models using the collection of statistical ML algorithms built into Amazon SageMaker.

⦁ Chapter 5, Training Computer Vision Models, shows you how to build and train models using the collection of computer vision algorithms built into Amazon SageMaker.

⦁ Chapter 6, Training Natural Language Processing Models, shows you how to build and train models using the collection of natural language processing algorithms built into Amazon SageMaker.

⦁ Chapter 7, Extending Machine Learning Services Using Built-In Frameworks, shows you how to build and train ML models using the collection of built-in open source frameworks in Amazon SageMaker.

⦁ Chapter 8, Using Your Algorithms and Code, shows you how to build and train ML models using your own code on Amazon SageMaker, for example, R or custom Python.

⦁ Chapter 9, Scaling Your Training Jobs, shows you how to distribute training jobs to many managed instances, using either built-in algorithms or built-in frameworks.

⦁ Chapter 10, Advanced Training Techniques, shows you how to leverage advanced training in Amazon SageMaker.

⦁ Chapter 11, Deploying Machine Learning Models, shows you how to deploy ML models in a variety of configurations.

⦁ Chapter 12, Automating Deployment Tasks, shows you how to automate the deployment of ML models on Amazon SageMaker.

⦁ Chapter 13, Optimizing Cost and Performance, shows you how to optimize model deployments, both from an infrastructure perspective and from a cost perspective.