English | 2022 | ISBN: ‎ 1801070156 | 327 pages | True PDF EPUB | 28.83 MB

Build production-grade machine learning models with SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code

Key Features

Understand the ML lifecycle in the cloud and its development on SageMaker Studio

Learn to apply SageMaker features in SageMaker Studio for ML use cases

Scale and operationalize the ML lifecycle effectively using SageMaker Studio

Book Description

SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature eeering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment.

In this book, you'll start by exploring the features available in SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio.

By the end of this book, you'll have learned ML best practices regarding SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases.

What you will learn

Explore the ML development life cycle in the cloud

Understand SageMaker Studio features and the user interface

Build a dataset with clicks and host a feature store for ML

Train ML models with ease and scale

Create ML models and solutions with little code

Host ML models in the cloud with optimal cloud resources

Ensure optimal model performance with model monitoring

Apply governance and operational excellence to ML projects

Who this book is for

This book is for data scientists and machine learning eeers who are looking to become well-versed with SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.

Table of Contents

Machine Learning and Its Life Cycle in the Cloud

Introducing SageMaker Studio

Data Preparation with SageMaker Data Wrangler

Building a Feature Repository with SageMaker Feature Store

Building and Training ML Models with SageMaker Studio IDE

Detecting ML Bias and Explaining Models with SageMaker Clarify

Hosting ML Models in the Cloud: Best Practices

Jumpstarting ML with SageMaker JumpStart and Autopilot

Training ML Models at Scale in SageMaker Studio

Monitoring ML Models in Production with SageMaker Model Monitor

Operationalize ML Projects with SageMaker Projects, Pipelines and Model Registry