A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker

Key Features

• Perform ML experiments with built-in and custom algorithms in SageMaker
• Explore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learn
• Use the different features and capabilities of SageMaker to automate relevant ML processes

Book Description

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.

This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.

By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.

What you will learn

• Train and deploy NLP, time series forecasting, and computer vision models to solve different business problems
• Push the limits of customization in SageMaker using custom container images
• Use AutoML capabilities with SageMaker Autopilot to create high-quality models
• Work with effective data analysis and preparation techniques
• Explore solutions for debugging and managing ML experiments and deployments
• Deal with bias detection and ML explainability requirements using SageMaker Clarify
• Automate intermediate and complex deployments and workflows using a variety of solutions

Who this book is for

This book is for developers, data scientists, and machine learning practitioners interested in using Amazon SageMaker to build, analyze, and deploy machine learning models with 80 step-by-step recipes. All you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.

Osoby które kupowały "Machine Learning with Amazon SageMaker Cookbook", wybierały także:

• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zÅ‚, (67,05 zÅ‚ -55%)
• Machine Learning i jÄ™zyk Python. Kurs video. Praktyczne wykorzystanie popularnych bibliotek 243,59 zÅ‚, (112,05 zÅ‚ -54%)
• Matematyka w deep learningu. Co musisz wiedzie 89,00 zÅ‚, (44,50 zÅ‚ -50%)
• Dylemat sztucznej inteligencji. 7 zasad odpowiedzialnego tworzenia technologii 54,90 zÅ‚, (27,45 zÅ‚ -50%)
• Eksploracja danych za pomoc 67,00 zÅ‚, (33,50 zÅ‚ -50%)

Spis treści

Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments eBook -- spis treści

• 1. Getting Started with Machine Learning Using Amazon SageMaker
• 2. Building and Using your own Algorithm Container Image
• 3. Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker
• 4. Preparing, Processing, and Analyzing the Data
• 5. Effectively Managing Machine Learning Experiments
• 6. Automated Machine Learning in Amazon SageMaker
• 7. Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor
• 8. Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms
• 9. Managing Machine Learning Workflows and Deployments