Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

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    About the author
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Preface
    Who this book is for
    What this book covers
    Download the example code files
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    Conventions used
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Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio  
Chapter 1: Machine Learning and Its Life Cycle in the Cloud
    Technical requirements
    Understanding ML and its life cycle
        An ML life cycle
    Building ML in the cloud
    Exploring AWS essentials for ML
        Compute
        Storage
        Database and analytics
        Security
    Setting up an AWS environment
    Summary
Chapter 2: Introducing Amazon SageMaker Studio
    Technical requirements
    Introducing SageMaker Studio and its components
        Prepare
        Build
        Training and tuning
        Deploy
        MLOps
    Setting up SageMaker Studio
        Setting up a domain
    Walking through the SageMaker Studio UI
        The main work area
        The sidebar
        "Hello world!" in SageMaker Studio
    Demystifying SageMaker Studio notebooks, instances, and kernels
    Using the SageMaker Python SDK
    Summary
Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio
Chapter 3: Data Preparation with SageMaker Data Wrangler
    Technical requirements
    Getting started with SageMaker Data Wrangler for customer churn prediction
        Preparing the use case
        Launching SageMaker Data Wrangler
    Importing data from sources
        Importing from S3
        Importing from Athena
        Editing the data type
        Joining tables
    Exploring data with visualization
        Understanding the frequency distribution with a histogram
        Scatter plots
        Previewing ML model performance with Quick Model
        Revealing target leakage
        Creating custom visualizations
    Applying transformation
        Exploring performance while wrangling
    Exporting data for ML training
    Summary
Chapter 4: Building a Feature Repository with SageMaker Feature Store
    Technical requirements
    Understanding the concept of a feature store
        Understanding an online store
        Understanding an offline store
    Getting started with SageMaker Feature Store
        Creating a feature group
        Ingesting data to SageMaker Feature Store 
        Ingesting from SageMaker Data Wrangler
    Accessing features from SageMaker Feature Store
        Accessing a feature group in the Studio UI
        Accessing an offline store – building a dataset for analysis and training
        Accessing online store – low-latency feature retrieval
    Summary
Chapter 5: Building and Training ML Models with SageMaker Studio IDE
    Technical requirements
    Training models with SageMaker's built-in algorithms
        Training an NLP model easily
        Managing training jobs with SageMaker Experiments
    Training with code written in popular frameworks 
        TensorFlow
        PyTorch
        Hugging Face
        MXNet
        Scikit-learn
    Developing and collaborating using SageMaker Notebook
    Summary
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
        Detecting pretraining bias
        Mitigating bias and training a model
        Detecting post-training bias
    Explaining ML models using SHAP values
    Summary
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
        Hosting multi-model endpoints to save costs
        Optimizing instance type and autoscaling with load testing
    Summary
Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot
    Technical requirements
    Launching a SageMaker JumpStart solution
        Solution catalog for industries
        Deploying the Product Defect Detection solution
    SageMaker JumpStart model zoo
        Model collection
        Deploying a model
        Fine-tuning a model
    Creating a high-quality model with SageMaker Autopilot
        Wine quality prediction
        Setting up an Autopilot job
        Understanding an Autopilot job
        Evaluating Autopilot models
    Summary
    Further reading
Part 3 – The Production and Operation of Machine Learning with SageMaker Studio
Chapter 9: Training ML Models at Scale in SageMaker Studio
    Technical requirements
    Performing distributed training in SageMaker Studio
        Understanding the concept of distributed training
        The data parallel library with TensorFlow
        Model parallelism with PyTorch
    Monitoring model training and compute resources with SageMaker Debugger
    Managing long-running jobs with checkpointing and spot training
    Summary
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
        Training and hosting a model
        Creating inference traffic and ground truth
        Creating a data quality monitor
        Creating a model quality monitor
    Reviewing model monitoring results in SageMaker Studio
    Summary
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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