AWS Services for Each Stage of the Machine Learning (ML) Pipeline
AWS offers a wide range of services to support each step of the ML lifecycle — from data preparation to model deployment and monitoring.
📥 1. Data Collection
Purpose: Gather and store raw data from various sources.
AWS Services:
- AWS Glue: ETL (Extract, Transform, Load) service to discover, catalog, and prepare data from various sources.
- Amazon S3: Durable and scalable object storage for storing raw and processed data.
- AWS Data Exchange: Securely find, subscribe to, and use third-party data in the cloud.
📊 2. Data Preparation & Exploration
Purpose: Clean, explore, and understand the data.
AWS Services:
- Amazon SageMaker Data Wrangler: Simplifies the process of data preparation and feature engineering with a visual interface.
- AWS Glue DataBrew: Visual data preparation tool for cleaning and normalizing data without writing code.
- AWS Lambda: Serverless compute for running preprocessing tasks and data transformations.
🧹 3. Feature Engineering
Purpose: Create and transform features that improve model performance.
AWS Services:
- Amazon SageMaker Feature Store – Centralized store for creating, storing, and sharing features across teams and models.
- SageMaker Processing Jobs – Run custom scripts for feature creation using Python, Spark, etc.
- AWS Lambda – Run lightweight, serverless feature transformation on-demand.
🧠 4. Model Training
Purpose: Train ML models using selected algorithms and data.
AWS Services:
- Amazon SageMaker: Fully managed service for building, training, and tuning machine learning models at scale.
- SageMaker Studio: Integrated development environment (IDE) for ML.
- SageMaker Experiments: Organizes and tracks ML experiments.
- SageMaker Debugger: Provides real-time insights into training jobs.
- AWS Batch: Run large-scale, parallel, or high-performance computing jobs.
- Amazon SageMaker Training Jobs – Scalable infrastructure for distributed training.
- Amazon SageMaker Autopilot – Automatically trains and tunes the best model with AutoML.
- Amazon SageMaker Studio – An integrated IDE for custom model development using Jupyter notebooks.
- SageMaker JumpStart – Pre-built solutions and model templates for common use cases.
⚙️ 5. Hyperparameter Tuning
Purpose: Optimize model performance by finding the best hyperparameters.
AWS Services:
- Amazon SageMaker Automatic Model Tuning – Searches for the best combination of hyperparameters using built-in algorithms.
📏 6. Model Evaluation
Purpose: Test the model's performance on validation or test data.
AWS Services:
- Amazon SageMaker Experiments – Track, compare, and manage model training runs and results.
- SageMaker Studio Notebooks – Visualize and evaluate models using metrics like accuracy, precision, and recall.
- Amazon SageMaker Model Monitor: Continuously monitors data quality and model performance, helping detect data drift and bias.
- Amazon SageMaker Clarify: Detects bias in datasets and models and provides explanations for model predictions.
🚀 7. Model Deployment
Purpose: Make the trained model available for real-time or batch inference.
AWS Services:
- Amazon SageMaker (Endpoints): Deploy models for real-time inference.
- Amazon SageMaker Batch Transform: Run large-scale, offline predictions on datasets.
- Amazon SageMaker Multi-Model Endpoints: Host multiple models on a single endpoint for cost efficiency.
- AWS Lambda: Deploy lightweight models for serverless inference.
- Amazon API Gateway: Create RESTful APIs to expose inference endpoints.
📡 8. Model Monitoring
Purpose: Monitor model performance in production and detect issues.
AWS Services:
- Amazon SageMaker Model Monitor – Detect data drift, prediction skew, and quality issues in real-time.
- Amazon CloudWatch – Collect logs and metrics for deployed models and services.
- AWS CloudTrail – Track API calls and audit model access or changes.
9. Automation & Orchestration
Purpose: Ensure the workflow is consistent, efficient, and scalable by eliminating manual steps and reducing errors, enabling faster and more reliable end-to-end machine learning operations.
AWS Services:
- Amazon SageMaker Pipelines: CI/CD service to automate and orchestrate end-to-end ML workflows.
🧠 Summary Table
| ML Pipeline Stage | Purpose | AWS Services & Features |
|---|---|---|
| Data Collection | Ingest and store raw data | Amazon S3, AWS Glue, Kinesis, AWS Data Exchange |
| Data Preparation & EDA | Clean and explore the data | SageMaker Data Wrangler, Glue DataBrew, Athena, QuickSight |
| Feature Engineering | Create useful input features | SageMaker Feature Store, Processing Jobs, AWS Lambda |
| Model Training | Train the model on data | SageMaker Training Jobs, Autopilot, Studio, JumpStart |
| Hyperparameter Tuning | Improve model performance | SageMaker Automatic Model Tuning |
| Model Evaluation | Measure model accuracy and performance | SageMaker Experiments, Studio Notebooks |
| Model Deployment | Make predictions available | SageMaker Endpoints, Batch Transform, Serverless, MME |
| Model Monitoring | Track model performance and drift | SageMaker Model Monitor, CloudWatch, CloudTrail |
| Automation & Orchestration | automate ML workflow | SageMaker Pipelines |