Learn Amazon SageMaker

Learn Amazon SageMaker
ePUB
  • eBook:
    Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for developers and data scientists
  • Author:
    Julien Simon, Francesco Pochetti
  • Edition:
    -
  • Categories:
  • Data:
    August 27, 2020
  • ISBN:
    180020891X
  • ISBN-13:
    9781800208919
  • Language:
    English
  • Pages:
    490 pages
  • Format:
    ePUB

Book Description
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

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

Book Description

Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker.
You'll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you'll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You'll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you'll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy.
By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation.

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

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.

Content

Section 1: Introduction to Amazon SageMaker
Chapter 1: Introduction to Amazon SageMaker
Chapter 2: Handling Data Preparation Techniques

Section 2: Building and Training Models
Chapter 3: AutoML with Amazon SageMaker Autopilot
Chapter 4: Training Machine Learning Models
Chapter 5: Training Computer Vision Models
Chapter 6: Training Natural Language Processing Models
Chapter 7: Extending Machine Learning Services Using Built-In Frameworks
Chapter 8: Using Your Algorithms and Code

Section 3: Diving Deeper on Training
Chapter 9: Scaling Your Training Jobs
Chapter 10: Advanced Training Techniques

Section 4: Managing Models in Production
Chapter 11: Deploying Machine Learning Models
Chapter 12: Automating Machine Learning Workflows
Chapter 13: Optimizing Prediction Cost and Performance

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