Here’s a comprehensive list of 100 chapter titles for learning Amazon SageMaker, arranged from beginner to advanced topics:
- Introduction to Amazon SageMaker
- Understanding Machine Learning (ML) and Its Applications
- How Amazon SageMaker Fits into the AWS Ecosystem
- Creating Your First SageMaker Account
- Setting Up AWS CLI and SageMaker Environment
- Navigating the Amazon SageMaker Console
- Overview of SageMaker Studio and Its Components
- What are Notebooks in SageMaker?
- Creating and Managing SageMaker Notebooks
- Working with Jupyter Notebooks in SageMaker
- Uploading and Storing Data in Amazon S3 for SageMaker
- Basics of AWS IAM for SageMaker Security
- Understanding SageMaker Training Jobs
- Overview of SageMaker Prebuilt Containers
- Setting Up SageMaker with Built-In Algorithms
- Running Your First Machine Learning Model in SageMaker
- Using SageMaker for Data Preprocessing
- Training a Model with SageMaker’s Built-In Algorithms
- Exploring SageMaker Model Hosting and Deployment
- Deploying Your First ML Model to SageMaker Endpoints
- How to Monitor Model Performance in SageMaker
- Introduction to SageMaker Ground Truth for Labeling Data
- Basics of SageMaker Pipelines for Workflow Automation
- SageMaker Experiments: Tracking Your Model Workflows
- Introduction to SageMaker Debugger for Model Training
- Getting Started with SageMaker Autopilot for AutoML
- How to Set Up SageMaker Model Monitoring
- Creating a Basic Model with SageMaker Estimators
- Deploying to Multi-Model Endpoints in SageMaker
- SageMaker Batch Transform for Batch Predictions
- Introduction to SageMaker Training and Tuning
- Hyperparameter Tuning with SageMaker Hyperparameter Optimization
- Using SageMaker for Custom Algorithm Training
- Using SageMaker Script Mode for Custom Code
- Managing Training Jobs and Resources in SageMaker
- Running Distributed Training Jobs on SageMaker
- Introduction to SageMaker Model Optimization
- Model Performance Tuning with SageMaker
- Deploying Machine Learning Models on SageMaker with Multiple Instances
- Creating Multi-Model Endpoints for Cost Efficiency
- Introduction to SageMaker Multi-Model Endpoints for Real-Time Inference
- Understanding SageMaker Asynchronous Inference
- Integrating SageMaker with Amazon Lambda Functions
- Using SageMaker to Build and Deploy Object Detection Models
- Running Large-Scale Training Jobs with SageMaker Distributed Training
- Working with SageMaker Model Monitor for Data Drift Detection
- Building and Deploying NLP Models Using SageMaker
- Building and Deploying Image Classification Models Using SageMaker
- Using SageMaker for Time Series Forecasting
- Introduction to SageMaker Reinforcement Learning
- Building and Deploying Custom TensorFlow Models with SageMaker
- Integrating SageMaker with AWS Glue for Data Wrangling
- Running SageMaker Jobs with AWS Fargate for Serverless Training
- Working with SageMaker to Create a Data Science Workflow
- How to Use SageMaker for Model Versioning
- Creating and Managing SageMaker Model Artifacts
- Deploying Pretrained Hugging Face Models with SageMaker
- Integrating SageMaker with Amazon Elastic Inference for Cost-Effective Inference
- Optimizing SageMaker Training with Spot Instances
- Leveraging SageMaker’s Data Parallelism for Efficient Training
- Understanding SageMaker’s Automatic Model Deployment
- Using SageMaker’s Built-in XGBoost for Training and Prediction
- How to Use SageMaker with Scikit-Learn for ML Models
- Training a Model with SageMaker Using Keras
- Integrating SageMaker with Apache MXNet for Deep Learning Models
- How to Automate Model Deployment with SageMaker Pipelines
- Creating and Managing Custom Environments with SageMaker
- Model Deployment with SageMaker Endpoint for High-Volume Use Cases
- Using SageMaker Multi-Model Endpoints for Resource Optimization
- Leveraging SageMaker for Real-Time Speech-to-Text Applications
- Advanced Model Training with SageMaker Distributed Frameworks
- Optimizing Hyperparameter Tuning Using SageMaker’s Bayesian Optimization
- Building and Deploying Generative Models Using SageMaker
- Deploying ML Models with SageMaker for Edge Devices (AWS IoT Greengrass)
- Optimizing Model Training with SageMaker’s Mixed Precision Training
- SageMaker for Large-Scale Deep Learning: Handling Big Data
- Advanced Model Deployment Strategies for Low-Latency Systems
- How to Use SageMaker for NLP Applications at Scale
- Building Custom ML Algorithms with SageMaker Script Mode
- Using SageMaker Studio for End-to-End ML Lifecycle Management
- Understanding SageMaker’s ML Model Interpretability Tools
- Leveraging SageMaker for Collaborative Data Science Teams
- Advanced Model Debugging with SageMaker Debugger
- Using SageMaker for Time-Sensitive Machine Learning Applications
- Model Explainability and Fairness with SageMaker Clarify
- Advanced Model Monitoring with SageMaker Model Monitor
- How to Use SageMaker for Recommender Systems
- Managing Data Pipelines with SageMaker Data Wrangler
- Building Complex Pipelines with SageMaker Pipelines for Automated ML Workflows
- Integrating SageMaker with Amazon Kinesis for Real-Time Data Streams
- Creating and Managing SageMaker Feature Stores
- Integrating SageMaker with Apache Spark for Scalable ML Workflows
- Deploying ML Models Across Multiple AWS Regions with SageMaker
- Running ML Jobs with SageMaker on GPU Instances for Deep Learning
- Advanced SageMaker AutoML for Custom Model Building
- Leveraging SageMaker for ML Model Auditing and Governance
- Building Custom Metrics and Alerts with SageMaker for Model Monitoring
- Creating Model Performance Dashboards with SageMaker and QuickSight
- Building and Deploying AI-Powered Applications Using SageMaker
- The Future of Amazon SageMaker: Emerging Technologies and Trends in ML
This progression takes you from the basic setup and use of Amazon SageMaker to advanced topics like deep learning, custom algorithm development, distributed training, and large-scale deployment. Each chapter reflects a skill-building approach to mastering the platform step by step.