Data Science on AWS Chris Fregly, Antje Barth

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Spis treści książki

• Preface
  • Overview of the Chapters
  • Who Should Read This Book
  • Other Resources
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to Data Science on AWS
  • Benefits of Cloud Computing
    • Agility
    • Cost Savings
    • Elasticity
    • Innovate Faster
    • Deploy Globally in Minutes
    • Smooth Transition from Prototype to Production
  • Data Science Pipelines and Workflows
    • Amazon SageMaker Pipelines
    • AWS Step Functions Data Science SDK
    • Kubeflow Pipelines
    • Managed Workflows for Apache Airflow on AWS
    • MLflow
    • TensorFlow Extended
    • Human-in-the-Loop Workflows
  • MLOps Best Practices
    • Operational Excellence
    • Security
    • Reliability
    • Performance Efficiency
    • Cost Optimization
  • Amazon AI Services and AutoML with Amazon SageMaker
    • Amazon AI Services
    • AutoML with SageMaker Autopilot
  • Data Ingestion, Exploration, and Preparation in AWS
    • Data Ingestion and Data Lakes with Amazon S3 and AWS Lake Formation
    • Data Analysis with Amazon Athena, Amazon Redshift, and Amazon QuickSight
    • Evaluate Data Quality with AWS Deequ and SageMaker Processing Jobs
    • Label Training Data with SageMaker Ground Truth
    • Data Transformation with AWS Glue DataBrew, SageMaker Data Wrangler, and SageMaker Processing Jobs
  • Model Training and Tuning with Amazon SageMaker
    • Train Models with SageMaker Training and Experiments
    • Built-in Algorithms
    • Bring Your Own Script (Script Mode)
    • Bring Your Own Container
    • Pre-Built Solutions and Pre-Trained Models with SageMaker JumpStart
    • Tune and Validate Models with SageMaker Hyper-Parameter Tuning
  • Model Deployment with Amazon SageMaker and AWS Lambda Functions
    • SageMaker Endpoints
    • SageMaker Batch Transform
    • Serverless Model Deployment with AWS Lambda
  • Streaming Analytics and Machine Learning on AWS
    • Amazon Kinesis Streaming
    • Amazon Managed Streaming for Apache Kafka
    • Streaming Predictions and Anomaly Detection
  • AWS Infrastructure and Custom-Built Hardware
    • SageMaker Compute Instance Types
    • GPUs and Amazon Custom-Built Compute Hardware
    • GPU-Optimized Networking and Custom-Built Hardware
    • Storage Options Optimized for Large-Scale Model Training
  • Reduce Cost with Tags, Budgets, and Alerts
  • Summary
• 2. Data Science Use Cases
  • Innovation Across Every Industry
  • Personalized Product Recommendations
    • Recommend Products with Amazon Personalize
    • Generate Recommendations with Amazon SageMaker and TensorFlow
    • Generate Recommendations with Amazon SageMaker and Apache Spark
  • Detect Inappropriate Videos with Amazon Rekognition
  • Demand Forecasting
    • Predict Energy Consumption with Amazon Forecast
    • Predict Demand for Amazon EC2 Instances with Amazon Forecast
  • Identify Fake Accounts with Amazon Fraud Detector
  • Enable Privacy-Leak Detection with Amazon Macie
  • Conversational Devices and Voice Assistants
    • Speech Recognition with Amazon Lex
    • Text-to-Speech Conversion with Amazon Polly
    • Speech-to-Text Conversion with Amazon Transcribe
  • Text Analysis and Natural Language Processing
    • Translate Languages with Amazon Translate
    • Classify Customer-Support Messages with Amazon Comprehend
    • Extract Resume Details with Amazon Textract and Comprehend
  • Cognitive Search and Natural Language Understanding
  • Intelligent Customer Support Centers
  • Industrial AI Services and Predictive Maintenance
  • Home Automation with AWS IoT and Amazon SageMaker
  • Extract Medical Information from Healthcare Documents
  • Self-Optimizing and Intelligent Cloud Infrastructure
    • Predictive Auto Scaling for Amazon EC2
    • Anomaly Detection on Streams of Data
  • Cognitive and Predictive Business Intelligence
    • Ask Natural-Language Questions with Amazon QuickSight
    • Train and Invoke SageMaker Models with Amazon Redshift
    • Invoke Amazon Comprehend and SageMaker Models from Amazon Aurora SQL Database
    • Invoke SageMaker Model from Amazon Athena
    • Run Predictions on Graph Data Using Amazon Neptune
  • Educating the Next Generation of AI and ML Developers
    • Build Computer Vision Models with AWS DeepLens
    • Learn Reinforcement Learning with AWS DeepRacer
    • Understand GANs with AWS DeepComposer
  • Program Natures Operating System with Quantum Computing
    • Quantum Bits Versus Digital Bits
    • Quantum Supremacy and the Quantum Computing Eras
    • Cracking Cryptography
    • Molecular Simulations and Drug Discovery
    • Logistics and Financial Optimizations
    • Quantum Machine Learning and AI
    • Programming a Quantum Computer with Amazon Braket
    • AWS Center for Quantum Computing
  • Increase Performance and Reduce Cost
    • Automatic Code Reviews with CodeGuru Reviewer
    • Improve Application Performance with CodeGuru Profiler
    • Improve Application Availability with DevOps Guru
  • Summary
• 3. Automated Machine Learning
  • Automated Machine Learning with SageMaker Autopilot
  • Track Experiments with SageMaker Autopilot
  • Train and Deploy a Text Classifier with SageMaker Autopilot
    • Train and Deploy with SageMaker Autopilot UI
    • Train and Deploy a Model with the SageMaker Autopilot Python SDK
    • Predict with Amazon Athena and SageMaker Autopilot
    • Train and Predict with Amazon Redshift ML and SageMaker Autopilot
  • Automated Machine Learning with Amazon Comprehend
    • Predict with Amazon Comprehends Built-in Model
    • Train and Deploy a Custom Model with the Amazon Comprehend UI
    • Train and Deploy a Custom Model with the Amazon Comprehend Python SDK
  • Summary
• 4. Ingest Data into the Cloud
  • Data Lakes
    • Import Data into the S3 Data Lake
    • Describe the Dataset
  • Query the Amazon S3 Data Lake with Amazon Athena
    • Access Athena from the AWS Console
    • Register S3 Data as an Athena Table
    • Update Athena Tables as New Data Arrives with AWS Glue Crawler
    • Create a Parquet-Based Table in Athena
  • Continuously Ingest New Data with AWS Glue Crawler
  • Build a Lake House with Amazon Redshift Spectrum
    • Export Amazon Redshift Data to S3 Data Lake as Parquet
    • Share Data Between Amazon Redshift Clusters
  • Choose Between Amazon Athena and Amazon Redshift
  • Reduce Cost and Increase Performance
    • S3 Intelligent-Tiering
    • Parquet Partitions and Compression
    • Amazon Redshift Table Design and Compression
    • Use Bloom Filters to Improve Query Performance
    • Materialized Views in Amazon Redshift Spectrum
  • Summary
• 5. Explore the Dataset
  • Tools for Exploring Data in AWS
  • Visualize Our Data Lake with SageMaker Studio
    • Prepare SageMaker Studio to Visualize Our Dataset
    • Run a Sample Athena Query in SageMaker Studio
    • Dive Deep into the Dataset with Athena and SageMaker
      • 1. Which product categories are the highest rated by average rating?
      • 2. Which product categories have the most reviews?
      • 3. When did each product category become available in the Amazon catalog?
      • 4. What is the breakdown of star ratings (15) per product category?
      • 5. How have the star ratings changed over time? Is there a drop-off point for certain product categories throughout the year?
      • 6. Which star ratings (15) are the most helpful?
      • 7. What is the distribution of review lengths (number of words)?
  • Query Our Data Warehouse
    • Run a Sample Amazon Redshift Query from SageMaker Studio
    • Dive Deep into the Dataset with Amazon Redshift and SageMaker
      • 1. Which product categories had the most reviews in 2015?
      • 2. Which products had the most helpful reviews in 2015?
      • 3. How did the star ratings change during 2015?
      • 4. Which customers wrote the most helpful reviews in 2015?
      • 5. Which customers provided more than one review for the same product in 2015?
  • Create Dashboards with Amazon QuickSight
  • Detect Data-Quality Issues with Amazon SageMaker and Apache Spark
    • SageMaker Processing Jobs
    • Analyze Our Dataset with Deequ and Apache Spark
  • Detect Bias in Our Dataset
    • Generate and Visualize Bias Reports with SageMaker Data Wrangler
    • Detect Bias with a SageMaker Clarify Processing Job
    • Integrate Bias Detection into Custom Scripts with SageMaker Clarify Open Source
    • Mitigate Data Bias by Balancing the Data
  • Detect Different Types of Drift with SageMaker Clarify
  • Analyze Our Data with AWS Glue DataBrew
  • Reduce Cost and Increase Performance
    • Use a Shared S3 Bucket for Nonsensitive Athena Query Results
    • Approximate Counts with HyperLogLog
    • Dynamically Scale a Data Warehouse with AQUA for Amazon Redshift
    • Improve Dashboard Performance with QuickSight SPICE
  • Summary
• 6. Prepare the Dataset for Model Training
  • Perform Feature Selection and Engineering
    • Select Training Features Based on Feature Importance
    • Balance the Dataset to Improve Model Accuracy
    • Split the Dataset into Train, Validation, and Test Sets
    • Transform Raw Text into BERT Embeddings
    • Convert Features and Labels to Optimized TensorFlow File Format
  • Scale Feature Engineering with SageMaker Processing Jobs
    • Transform with scikit-learn and TensorFlow
    • Transform with Apache Spark and TensorFlow
  • Share Features Through SageMaker Feature Store
    • Ingest Features into SageMaker Feature Store
    • Retrieve Features from SageMaker Feature Store
  • Ingest and Transform Data with SageMaker Data Wrangler
  • Track Artifact and Experiment Lineage with Amazon SageMaker
    • Understand Lineage-Tracking Concepts
    • Show Lineage of a Feature Engineering Job
    • Understand the SageMaker Experiments API
  • Ingest and Transform Data with AWS Glue DataBrew
  • Summary
• 7. Train Your First Model
  • Understand the SageMaker Infrastructure
    • Introduction to SageMaker Containers
    • Increase Availability with Compute and Network Isolation
  • Deploy a Pre-Trained BERT Model with SageMaker JumpStart
  • Develop a SageMaker Model
    • Built-in Algorithms
    • Bring Your Own Script
    • Bring Your Own Container
  • A Brief History of Natural Language Processing
  • BERT Transformer Architecture
  • Training BERT from Scratch
    • Masked Language Model
    • Next Sentence Prediction
  • Fine Tune a Pre-Trained BERT Model
  • Create the Training Script
    • Setup the Train, Validation, and Test Dataset Splits
    • Set Up the Custom Classifier Model
    • Train and Validate the Model
    • Save the Model
  • Launch the Training Script from a SageMaker Notebook
    • Define the Metrics to Capture and Monitor
    • Configure the Hyper-Parameters for Our Algorithm
    • Select Instance Type and Instance Count
    • Putting It All Together in the Notebook
    • Download and Inspect Our Trained Model from S3
    • Show Experiment Lineage for Our SageMaker Training Job
    • Show Artifact Lineage for Our SageMaker Training Job
  • Evaluate Models
    • Run Some Ad Hoc Predictions from the Notebook
    • Analyze Our Classifier with a Confusion Matrix
    • Visualize Our Neural Network with TensorBoard
    • Monitor Metrics with SageMaker Studio
    • Monitor Metrics with CloudWatch Metrics
  • Debug and Profile Model Training with SageMaker Debugger
    • Detect and Resolve Issues with SageMaker Debugger Rules and Actions
    • Profile Training Jobs
  • Interpret and Explain Model Predictions
  • Detect Model Bias and Explain Predictions
    • Detect Bias with a SageMaker Clarify Processing Job
    • Feature Attribution and Importance with SageMaker Clarify and SHAP
  • More Training Options for BERT
    • Convert TensorFlow BERT Model to PyTorch
    • Train PyTorch BERT Models with SageMaker
    • Train Apache MXNet BERT Models with SageMaker
    • Train BERT Models with PyTorch and AWS Deep Java Library
  • Reduce Cost and Increase Performance
    • Use Small Notebook Instances
    • Test Model-Training Scripts Locally in the Notebook
    • Profile Training Jobs with SageMaker Debugger
    • Start with a Pre-Trained Model
    • Use 16-Bit Half Precision and bfloat16
    • Mixed 32-Bit Full and 16-Bit Half Precision
    • Quantization
    • Use Training-Optimized Hardware
    • Spot Instances and Checkpoints
    • Early Stopping Rule in SageMaker Debugger
  • Summary
• 8. Train and Optimize Models at Scale
  • Automatically Find the Best Model Hyper-Parameters
    • Set Up the Hyper-Parameter Ranges
    • Run the Hyper-Parameter Tuning Job
    • Analyze the Best Hyper-Parameters from the Tuning Job
    • Show Experiment Lineage for Our SageMaker Tuning Job
  • Use Warm Start for Additional SageMaker Hyper-Parameter Tuning Jobs
    • Run HPT Job Using Warm Start
    • Analyze the Best Hyper-Parameters from the Warm-Start Tuning Job
  • Scale Out with SageMaker Distributed Training
    • Choose a Distributed-Communication Strategy
    • Choose a Parallelism Strategy
    • Choose a Distributed File System
    • Launch the Distributed Training Job
  • Reduce Cost and Increase Performance
    • Start with Reasonable Hyper-Parameter Ranges
    • Shard the Data with ShardedByS3Key
    • Stream Data on the Fly with Pipe Mode
    • Enable Enhanced Networking
  • Summary
• 9. Deploy Models to Production
  • Choose Real-Time or Batch Predictions
  • Real-Time Predictions with SageMaker Endpoints
    • Deploy Model Using SageMaker Python SDK
    • Track Model Deployment in Our Experiment
    • Analyze the Experiment Lineage of a Deployed Model
    • Invoke Predictions Using the SageMaker Python SDK
    • Invoke Predictions Using HTTP POST
    • Create Inference Pipelines
    • Invoke SageMaker Models from SQL and Graph-Based Queries
  • Auto-Scale SageMaker Endpoints Using Amazon CloudWatch
    • Define a Scaling Policy with AWS-Provided Metrics
    • Define a Scaling Policy with a Custom Metric
    • Tuning Responsiveness Using a Cooldown Period
    • Auto-Scale Policies
  • Strategies to Deploy New and Updated Models
    • Split Traffic for Canary Rollouts
    • Shift Traffic for Blue/Green Deployments
  • Testing and Comparing New Models
    • Perform A/B Tests to Compare Model Variants
    • Reinforcement Learning with Multiarmed Bandit Testing
  • Monitor Model Performance and Detect Drift
    • Enable Data Capture
    • Understand Baselines and Drift
  • Monitor Data Quality of Deployed SageMaker Endpoints
    • Create a Baseline to Measure Data Quality
    • Schedule Data-Quality Monitoring Jobs
    • Inspect Data-Quality Results
  • Monitor Model Quality of Deployed SageMaker Endpoints
    • Create a Baseline to Measure Model Quality
    • Schedule Model-Quality Monitoring Jobs
    • Inspect Model-Quality Monitoring Results
  • Monitor Bias Drift of Deployed SageMaker Endpoints
    • Create a Baseline to Detect Bias
    • Schedule Bias-Drift Monitoring Jobs
    • Inspect Bias-Drift Monitoring Results
  • Monitor Feature Attribution Drift of Deployed SageMaker Endpoints
    • Create a Baseline to Monitor Feature Attribution
    • Schedule Feature Attribution Drift Monitoring Jobs
    • Inspect Feature Attribution Drift Monitoring Results
  • Perform Batch Predictions with SageMaker Batch Transform
    • Select an Instance Type
    • Set Up the Input Data
    • Tune the SageMaker Batch Transform Configuration
    • Prepare the SageMaker Batch Transform Job
    • Run the SageMaker Batch Transform Job
    • Review the Batch Predictions
  • AWS Lambda Functions and Amazon API Gateway
  • Optimize and Manage Models at the Edge
  • Deploy a PyTorch Model with TorchServe
  • TensorFlow-BERT Inference with AWS Deep Java Library
  • Reduce Cost and Increase Performance
    • Delete Unused Endpoints and Scale In Underutilized Clusters
    • Deploy Multiple Models in One Container
    • Attach a GPU-Based Elastic Inference Accelerator
    • Optimize a Trained Model with SageMaker Neo and TensorFlow Lite
    • Use Inference-Optimized Hardware
  • Summary
• 10. Pipelines and MLOps
  • Machine Learning Operations
  • Software Pipelines
  • Machine Learning Pipelines
    • Components of Effective Machine Learning Pipelines
    • Steps of an Effective Machine Learning Pipeline
  • Pipeline Orchestration with SageMaker Pipelines
    • Create an Experiment to Track Our Pipeline Lineage
    • Define Our Pipeline Steps
    • Configure the Pipeline Parameters
    • Create the Pipeline
    • Start the Pipeline with the Python SDK
    • Start the Pipeline with the SageMaker Studio UI
    • Approve the Model for Staging and Production
    • Review the Pipeline Artifact Lineage
    • Review the Pipeline Experiment Lineage
  • Automation with SageMaker Pipelines
    • GitOps Trigger When Committing Code
    • S3 Trigger When New Data Arrives
    • Time-Based Schedule Trigger
    • Statistical Drift Trigger
  • More Pipeline Options
    • AWS Step Functions and the Data Science SDK
    • Kubeflow Pipelines
    • Apache Airflow
    • MLflow
    • TensorFlow Extended
  • Human-in-the-Loop Workflows
    • Improving Model Accuracy with Amazon A2I
    • Active-Learning Feedback Loops with SageMaker Ground Truth
  • Reduce Cost and Improve Performance
    • Cache Pipeline Steps
    • Use Less-Expensive Spot Instances
  • Summary
• 11. Streaming Analytics and Machine Learning
  • Online Learning Versus Offline Learning
  • Streaming Applications
  • Windowed Queries on Streaming Data
    • Stagger Windows
    • Tumbling Windows
    • Sliding Windows
  • Streaming Analytics and Machine Learning on AWS
  • Classify Real-Time Product Reviews with Amazon Kinesis, AWS Lambda, and Amazon SageMaker
  • Implement Streaming Data Ingest Using Amazon Kinesis Data Firehose
    • Create Lambda Function to Invoke SageMaker Endpoint
    • Create the Kinesis Data Firehose Delivery Stream
    • Put Messages on the Stream
  • Summarize Real-Time Product Reviews with Streaming Analytics
  • Setting Up Amazon Kinesis Data Analytics
    • Create a Kinesis Data Stream to Deliver Data to a Custom Application
    • Create AWS Lambda Function to Send Notifications via Amazon SNS
    • Create AWS Lambda Function to Publish Metrics to Amazon CloudWatch
    • Transform Streaming Data in Kinesis Data Analytics
    • Understand In-Application Streams and Pumps
  • Amazon Kinesis Data Analytics Applications
    • Calculate Average Star Rating
    • Detect Anomalies in Streaming Data
    • Calculate Approximate Counts of Streaming Data
    • Create Kinesis Data Analytics Application
    • Start the Kinesis Data Analytics Application
    • Put Messages on the Stream
  • Classify Product Reviews with Apache Kafka, AWS Lambda, and Amazon SageMaker
  • Reduce Cost and Improve Performance
    • Aggregate Messages
    • Consider Kinesis Firehose Versus Kinesis Data Streams
    • Enable Enhanced Fan-Out for Kinesis Data Streams
  • Summary
• 12. Secure Data Science on AWS
  • Shared Responsibility Model Between AWS and Customers
  • Applying AWS Identity and Access Management
    • IAM Users
    • IAM Policies
    • IAM User Roles
    • IAM Service Roles
    • Specifying Condition Keys for IAM Roles
    • Enable Multifactor Authentication
    • Least Privilege Access with IAM Roles and Policies
    • Resource-Based IAM Policies
    • Identity-Based IAM Policies
  • Isolating Compute and Network Environments
    • Virtual Private Cloud
    • VPC Endpoints and PrivateLink
    • Limiting Athena APIs with a VPC Endpoint Policy
  • Securing Amazon S3 Data Access
    • Require a VPC Endpoint with an S3 Bucket Policy
    • Limit S3 APIs for an S3 Bucket with a VPC Endpoint Policy
    • Restrict S3 Bucket Access to a Specific VPC with an S3 Bucket Policy
    • Limit S3 APIs with an S3 Bucket Policy
    • Restrict S3 Data Access Using IAM Role Policies
    • Restrict S3 Bucket Access to a Specific VPC with an IAM Role Policy
    • Restrict S3 Data Access Using S3 Access Points
  • Encryption at Rest
    • Create an AWS KMS Key
    • Encrypt the Amazon EBS Volumes During Training
    • Encrypt the Uploaded Model in S3 After Training
    • Store Encryption Keys with AWS KMS
    • Enforce S3 Encryption for Uploaded S3 Objects
    • Enforce Encryption at Rest for SageMaker Jobs
    • Enforce Encryption at Rest for SageMaker Notebooks
    • Enforce Encryption at Rest for SageMaker Studio
  • Encryption in Transit
    • Post-Quantum TLS Encryption in Transit with KMS
    • Encrypt Traffic Between Training-Cluster Containers
    • Enforce Inter-Container Encryption for SageMaker Jobs
  • Securing SageMaker Notebook Instances
    • Deny Root Access Inside SageMaker Notebooks
    • Disable Internet Access for SageMaker Notebooks
  • Securing SageMaker Studio
    • Require a VPC for SageMaker Studio
    • SageMaker Studio Authentication
  • Securing SageMaker Jobs and Models
    • Require a VPC for SageMaker Jobs
    • Require Network Isolation for SageMaker Jobs
  • Securing AWS Lake Formation
  • Securing Database Credentials with AWS Secrets Manager
  • Governance
    • Secure Multiaccount AWS Environments with AWS Control Tower
    • Manage Accounts with AWS Organizations
    • Enforce Account-Level Permissions with SCPs
    • Implement Multiaccount Model Deployments
  • Auditability
    • Tag Resources
    • Log Activities and Collect Events
    • Track User Activity and API Calls
  • Reduce Cost and Improve Performance
    • Limit Instance Types to Control Cost
    • Quarantine or Delete Untagged Resources
    • Use S3 Bucket KMS Keys to Reduce Cost and Increase Performance
  • Summary
• Index

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