Book Image

Getting Started with Amazon SageMaker Studio

By : Michael Hsieh
Book Image

Getting Started with Amazon SageMaker Studio

By: Michael Hsieh

Overview of this book

Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, 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 Amazon 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 Amazon 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.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
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1
Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio
Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio
Free Chapter
2
Chapter 1: Machine Learning and Its Life Cycle in the Cloud
Chapter 1: Machine Learning and Its Life Cycle in the Cloud
Technical requirements
Understanding ML and its life cycle
Building ML in the cloud
Exploring AWS essentials for ML
Setting up an AWS environment
Summary
3
Chapter 2: Introducing Amazon SageMaker Studio
Chapter 2: Introducing Amazon SageMaker Studio
Technical requirements
Introducing SageMaker Studio and its components
Setting up SageMaker Studio
Walking through the SageMaker Studio UI
Demystifying SageMaker Studio notebooks, instances, and kernels
Using the SageMaker Python SDK
Summary
4
Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio
Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio
5
Chapter 3: Data Preparation with SageMaker Data Wrangler
Chapter 3: Data Preparation with SageMaker Data Wrangler
Technical requirements
Getting started with SageMaker Data Wrangler for customer churn prediction
Importing data from sources
Exploring data with visualization
Applying transformation
Exporting data for ML training
Summary
6
Chapter 4: Building a Feature Repository with SageMaker Feature Store
Chapter 4: Building a Feature Repository with SageMaker Feature Store
Technical requirements
Understanding the concept of a feature store
Getting started with SageMaker Feature Store
Accessing features from SageMaker Feature Store
Summary
7
Chapter 5: Building and Training ML Models with SageMaker Studio IDE
Chapter 5: Building and Training ML Models with SageMaker Studio IDE
Technical requirements
Training models with SageMaker's built-in algorithms
Training with code written in popular frameworks
Developing and collaborating using SageMaker Notebook
Summary
8
Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify
Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify
Technical requirements
Understanding bias, fairness in ML, and ML explainability
Detecting bias in ML
Explaining ML models using SHAP values
Summary
9
Chapter 7: Hosting ML Models in the Cloud: Best Practices
Chapter 7: Hosting ML Models in the Cloud: Best Practices
Technical requirements
Deploying models in the cloud after training
Inferencing in batches with batch transform
Hosting real-time endpoints
Optimizing your model deployment
Summary
10
Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot
Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot
Technical requirements
Launching a SageMaker JumpStart solution
SageMaker JumpStart model zoo
Creating a high-quality model with SageMaker Autopilot
Summary
Further reading
11
Part 3 – The Production and Operation of Machine Learning with SageMaker Studio
Part 3 – The Production and Operation of Machine Learning with SageMaker Studio
12
Chapter 9: Training ML Models at Scale in SageMaker Studio
Chapter 9: Training ML Models at Scale in SageMaker Studio
Technical requirements
Performing distributed training in SageMaker Studio
Monitoring model training and compute resources with SageMaker Debugger
Managing long-running jobs with checkpointing and spot training
Summary
13
Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor
Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor
Technical requirements
Understanding drift in ML
Monitoring data and performance drift in SageMaker Studio
Reviewing model monitoring results in SageMaker Studio
Summary
14
Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry
Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry
Technical requirements
Understanding ML operations and CI/CD
Creating a SageMaker project
Orchestrating an ML pipeline with SageMaker Pipelines
Running CI/CD in SageMaker Studio
Summary
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15
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Chapter 1: Machine Learning and Its Life Cycle in the Cloud

Machine Learning (ML) is a technique that has been around for decades. It is hard to believe how ubiquitous ML is now in our daily life. It has also been a rocky road for the field of ML to become mainstream, until the recent major leap in computer technology. Today's computer hardware is faster, smaller, and smarter. Internet speeds are faster and more convenient. Storage is cheaper and smaller. Now, it is rather easy to collect, store, and process massive amounts of data with the technology we have now. We are able to create sizeable datasets that we were not able to before, train ML models using compute resources that were not available before, and make use of ML models in every corner of our lives.

For example, media streaming companies can now build ML recommendation engines at a global scale using their title collections and customer activity data on their websites to provide the most relevant content in real time in order to optimize the customer experience. The size of the data for both the titles and customer preferences and activity is on a scale that wasn't possible 20 years ago, considering how many of us are currently using a streaming service.

Training an ML model at this scale, using ML algorithms that are becoming increasingly more complex, requires a robust and scalable solution. After a model is trained, companies are able to serve the model at a global scale where millions of users visit the application from web and mobile devices at the same time.

Companies are also creating more and more models for each segment of customers or even one model for one customer. There is another dimension to this – companies are rolling out new models at a pace that would not have been possible to manage without a pipeline that trains, evaluates, tests, and deploys a new model automatically. Cloud computing has provided a perfect foundation for the streaming service provider to perform these ML activities to increase customer satisfaction.

If ML is something that interests you, or if you are already working in the field of ML in any capacity, this book is the right place for you. You will be learning all things ML, and how to build, train, host, and manage ML models in the cloud with actual use cases and datasets along with me throughout the book. I assume you come to this book with a good understanding of ML and cloud computing. The purpose of this first chapter is to set the level of the concepts and terminology of the two technologies, to define the ML life cycle that is going to be the core of this book, and to provide a crash course on Amazon Web Services and its core services, which will be mentioned throughout the book.

In this chapter, we will cover the following:

  • Understanding ML and its life cycle
  • Building ML in the cloud
  • Exploring AWS essentials for ML
  • Setting up AWS environment
Previous Section
End of Section 1
Next Section