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
Share Your Thoughts
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
Why subscribe?
15
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Building ML in the cloud

Cloud computing is a technology that delivers on-demand IT resources that can grow and shrink at any time, depending on the need. There is no more buying and maintaining computer servers or data centers. It is much like utilities in your home, such as water, which is there when you turn on the faucet. If you turn it all the way, you get a high-pressure water stream. If you turn it down, you conserve water. If you don't need it anymore, you turn it off completely. With this model, developers and teams get the following benefits from on-demand cloud computing:

  • Agility: Quickly spin up resources as you need them. Develop and roll out new apps, experiment with new ideas, and fail quickly without risks.
  • Elasticity: Scale your resources as you need them. Cloud computing takes away "undifferentiated heavy lifting" – racking up additional servers and planning capacity for the future. These are things that don't help address your core business problems.
  • Global availability: With a click of a button, you can spin up resources that are closest to your customers/users without relocating your physical compute resources.

How does this impact the field of ML? As compute resources become easier to acquire, information exchange becomes much more frequent. As that happens, more data is generated and stored. And more data means more opportunities to train more accurate ML models. The agility, elasticity, and scale that cloud computing provides accelerates the development and application of ML models from weeks or months down to a much shorter cycle so that developers can now generate and improve ML models faster than ever. Developers are no longer constrained by physical compute resources available to them. With better ML models, businesses can make better decisions and provide better product experiences to customers.

For cloud computing, we will be using Amazon Web Services, which is the provider of Amazon SageMaker Studio, throughout the book.

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