Here’s a comprehensive list of 100 chapter titles for a guide on AWS SageMaker with a focus on artificial intelligence (AI), ranging from beginner to advanced topics:
¶ Introduction to AWS SageMaker and AI (Beginner)
- Introduction to AWS SageMaker and Its Role in AI
- What is Machine Learning and How Does AWS SageMaker Simplify AI?
- Setting Up Your AWS SageMaker Environment for AI Projects
- AWS SageMaker Overview: Key Features and Benefits for AI Workflows
- Navigating the AWS SageMaker Console for AI Projects
- The Architecture of AWS SageMaker for AI Model Development
- How SageMaker Facilitates the End-to-End Machine Learning Lifecycle
- AWS SageMaker Terminology: Understanding Models, Endpoints, and Pipelines
- Key Differences Between AWS SageMaker and Other Machine Learning Platforms
- Exploring AWS SageMaker’s Integration with AWS Services for AI
- Introduction to Supervised and Unsupervised Learning with AWS SageMaker
- Working with AWS SageMaker Built-in Algorithms for AI Applications
- Preparing Data for Machine Learning in AWS SageMaker
- Data Preprocessing and Feature Engineering in AWS SageMaker
- Creating and Training Your First Model Using SageMaker’s Built-In Algorithms
- Evaluating AI Models with AWS SageMaker Metrics and Diagnostics
- Deploying Your First Model Using SageMaker Endpoints
- Monitoring Model Performance and Logs in SageMaker
- Introduction to SageMaker Studio for AI Development
- Understanding SageMaker Notebooks for Prototyping and Experimentation
- Advanced Model Training in AWS SageMaker with Custom Algorithms
- Using SageMaker for Distributed Training and Parallel Processing
- Hyperparameter Optimization in SageMaker for Better AI Models
- Transfer Learning in AWS SageMaker: Accelerating AI Development
- Using SageMaker to Train Neural Networks for AI Applications
- Real-Time AI Inference with SageMaker Endpoints
- Scaling Model Training with SageMaker Distributed Training
- Using SageMaker Multi-Model Endpoints for Efficient AI Deployment
- Model Versioning and Management in AWS SageMaker
- Integrating AWS SageMaker with AWS Lambda for Serverless AI Applications
- Introduction to Deep Learning in AWS SageMaker
- Training Deep Neural Networks with AWS SageMaker
- Using SageMaker to Train Convolutional Neural Networks (CNNs) for Computer Vision
- Building Recurrent Neural Networks (RNNs) with AWS SageMaker for AI
- Implementing Transfer Learning for Deep Learning Models in SageMaker
- Fine-Tuning Pre-Trained Models with AWS SageMaker for AI Applications
- Hyperparameter Optimization for Deep Learning Models in SageMaker
- Deploying TensorFlow Models with AWS SageMaker for Real-Time Inference
- Building AI-powered Image Classification Models Using SageMaker
- Using SageMaker with PyTorch for Deep Learning AI Workflows
- Introduction to SageMaker Pipelines for Automating AI Workflows
- Building End-to-End Machine Learning Pipelines in AWS SageMaker
- Automating Data Preprocessing and Feature Engineering in SageMaker Pipelines
- Deploying Machine Learning Models in Production with SageMaker Pipelines
- Integrating SageMaker Pipelines with AWS Step Functions for AI Workflows
- Using SageMaker Studio for Pipeline Visualization and Management
- Monitoring and Logging AI Pipelines with SageMaker
- Leveraging SageMaker for Continuous Model Training and Deployment
- Best Practices for Version Control in SageMaker Pipelines
- Managing Model Drift and Retraining with SageMaker Pipelines
¶ AI Model Deployment and Management (Advanced)
- Advanced Model Deployment Strategies with AWS SageMaker
- Using SageMaker for Real-Time and Batch Inference
- Setting Up Multi-Model Endpoints in AWS SageMaker for Efficient Inference
- Automating AI Model Deployment with SageMaker Model Monitor
- Integrating AWS SageMaker with AWS Elastic Inference for Cost-Effective AI
- Continuous Integration and Delivery (CI/CD) for AI Models in AWS SageMaker
- A/B Testing for AI Models with SageMaker Endpoint Versions
- Managing AI Model Life Cycle and Retraining in SageMaker
- Scaling AI Deployments with SageMaker Multi-Model Endpoints
- Monitoring and Updating Models in Production Using SageMaker
- Optimizing AI Models for Edge Devices with AWS SageMaker Neo
- Reducing Latency in AI Inference with SageMaker Multi-Model Endpoints
- Using SageMaker Automatic Model Tuning for Hyperparameter Optimization
- Model Pruning and Quantization in AWS SageMaker for AI Efficiency
- Deploying Optimized Models to Mobile and IoT Devices with SageMaker
- Optimizing Deep Learning Models for Performance and Cost with SageMaker
- Leveraging SageMaker Neo for Cross-Platform AI Model Deployment
- Auto-scaling AI Models on AWS Using SageMaker and ECS
- Reducing Cost of AI Inference with SageMaker Elastic Inference
- Best Practices for Efficient Model Deployment and Inference with AWS SageMaker
¶ AI Security, Privacy, and Governance with AWS SageMaker (Advanced)
- Ensuring Data Privacy in Machine Learning Workflows with AWS SageMaker
- Securing SageMaker Endpoints for AI Model Inference
- Using SageMaker with AWS Identity and Access Management (IAM) for AI Security
- Implementing Encryption for AI Models and Data in AWS SageMaker
- Auditing AI Models with SageMaker Model Monitor for Compliance
- Best Practices for Secure Model Deployment and Management in SageMaker
- Automating Compliance Reporting for AI Models in AWS SageMaker
- Data Masking and Redaction in SageMaker for Sensitive AI Data
- Using SageMaker for Explainability and Fairness in AI Models
- Managing AI Governance and Accountability in AWS SageMaker
- Tracking Machine Learning Experiments with SageMaker Experiments
- Managing and Comparing Multiple Model Versions in SageMaker
- Using SageMaker Debugger for Real-Time Debugging of AI Models
- Visualizing and Interpreting AI Model Performance in SageMaker Studio
- Creating Custom Metrics and Logs for AI Experimentation in SageMaker
- Advanced Hyperparameter Tuning with SageMaker Automatic Model Tuning
- Running Large-Scale Experiments with SageMaker for AI Model Evaluation
- Comparing Model Performance Across Different Algorithms in SageMaker
- Using SageMaker with AWS CloudWatch for Detailed AI Monitoring
- Collaborative Experimentation with SageMaker Studio Notebooks
- Using SageMaker with TensorFlow Extended (TFX) for Production Pipelines
- Integrating SageMaker with MLflow for Tracking AI Model Metadata
- Building Serverless AI Applications with AWS Lambda and SageMaker
- Using SageMaker for Reinforcement Learning in AI Applications
- Combining SageMaker with Apache Spark for Scalable AI Workflows
- Building Conversational AI Models with AWS SageMaker and Amazon Lex
- Using SageMaker to Deploy AI Models in Hybrid and Multi-Cloud Environments
- Integrating SageMaker with AWS Glue for Advanced ETL in AI Projects
- Real-Time AI Monitoring with SageMaker and AWS CloudTrail
- Leveraging SageMaker with Amazon Aurora for Scalable AI Data Storage Solutions
These chapter titles cover everything from the basics of AWS SageMaker and machine learning concepts to advanced topics like model optimization, deployment strategies, security, governance, and integration with other AWS services. They are designed to guide learners through each step of utilizing AWS SageMaker for building, training, deploying, and managing AI applications efficiently and at scale.