Book Image

Learn Amazon SageMaker

By : Julien Simon
Book Image

Learn Amazon SageMaker

By: Julien Simon

Overview of this book

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.
Table of Contents (19 chapters)
Section 1: Introduction to Amazon SageMaker
Section 2: Building and Training Models
Section 3: Diving Deeper on Training
Section 4: Managing Models in Production

Exploring models with SageMaker Debugger

SageMaker Debugger lets you configure debugging rules for your training job. These rules will inspect its internal state and check for specific unwanted conditions that could be developing during training. SageMaker Debugger includes a long list of built-in rules (The loss not decreasing, vanishing gradients, and so on), and you can write your own

Here are some of the built-in rules: all_zero, class_imbalance, confusion, loss_not_decreasing, overfit, overtraining, similar_across_runs, stalled_training_rule, tensor_variance, and unchanged_tensor. You can see the full list at

In addition, you can save and inspect the model state (gradients, weights, and so on)as well as the training state (metrics, optimizer parameters, and so on). At each training step, the tensors storing these values may be saved in near real time in an S3 bucket, making it possible to visualize...