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

Preparing natural language datasets

For the CV algorithms in the previous chapter, data preparation focused on the technical format required for the dataset (Image format, RecordIO, or augmented manifest). The images themselves weren't processed.

Things are quite different for NLP algorithms. Text needs to be heavily processed, converted, and saved in the right format. In most learning resources, these steps are abbreviated or even ignored. Data is already "automagically" ready for training, leaving the reader frustrated and sometimes dumbfounded on how to prepare their own datasets.

No such thing here! In this section, you'll learn how to prepare NLP datasets in different formats. Once again, get ready to learn a lot!

Let's start with preparing data for BlazingText.

Preparing data for classification with BlazingText

BlazingText expects labeled input data in the same format as FastText:

  • A plain text file, with one sample per line.
  • ...