The Challenge
Teams need hands-on practice building an ML platform with ML workflows, orchestration, monitoring, and production deployment patterns.
What You'll Build
- → SageMaker notebooks for data science
- → Complete ML pipeline automation
- → Model training with hyperparameter tuning
- → Production endpoints with auto-scaling
- → Data drift detection and monitoring
Try This Prompt in Pulumi Neo
Edit the prompt below and run it directly in Neo to deploy your infrastructure.
Best For
Use this guide to build an ML platform. Perfect for learning ML workflows, SageMaker, orchestration, and production ML deployment.
Learning Objectives
This guide covers:
- ML Workflows - End-to-end pipelines
- Training - SageMaker jobs
- Deployment - Production endpoints
- Monitoring - Model drift detection
- Orchestration - Step Functions
Advanced scenario for ML platform!