Skip to main content

​
Overview

This module explores the strategic decisions and practical implementations involved in deploying machine learning systems on cloud platforms. You’ll learn how to evaluate different ML platforms, make informed buy vs build decisions, and implement advanced deployment patterns like multi-model endpoints.

​
Key Topics

​
Buy vs Build Decision Framework

Learn a structured approach to evaluating whether to:
  • Build custom ML infrastructure from scratch
  • Adopt managed ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
  • Use hybrid approaches combining both strategies
The decision framework covers:
  • Cost analysis (development, maintenance, operations)
  • Time to market considerations
  • Team expertise and resources
  • Flexibility and customization needs
  • Vendor lock-in risks

​
Cloud ML Platforms

Explore the major managed ML platforms: AWS SageMaker
  • End-to-end ML platform for building, training, and deploying models
  • Processing jobs for data preparation
  • Model hosting with various deployment options
  • Integration with AWS ecosystem (S3, RDS, EC2)
GCP Vertex AI
  • Unified ML platform on Google Cloud
  • AutoML capabilities
  • Custom training and prediction
  • Integration with Google Cloud services

​
Multi-Model Endpoints

Implement advanced deployment patterns that allow hosting multiple models behind a single endpoint:
  • Cost optimization through resource sharing
  • Simplified infrastructure management
  • Dynamic model loading and unloading
  • Support for different model types (image, text, tabular)

​
Example AWS MLOps Stack

- Infrastructure: AWS
- Data Storage: S3 + RDS
- Experiments: EC2 + SageMaker Processing Jobs
- Pipelines: MWAA (Managed Workflows for Apache Airflow)
- Basic Deployment: SageMaker Inference Toolkit
- Advanced Deployment: Asynchronous Inference + Multi-Model Endpoints
- Monitoring: SageMaker Model Monitor

​
Learning Outcomes

By the end of this module, you will be able to:
  1. Evaluate platforms using a structured decision framework
  2. Deploy multi-model endpoints on AWS SageMaker
  3. Compare cloud platforms based on features, cost, and fit
  4. Design MLOps architectures using managed services
  5. Implement production patterns like async inference and model monitoring

​
Reference Materials

​
Next Steps

Buy vs Build Framework

Learn the decision framework for evaluating ML platforms

AWS SageMaker

Deploy multi-model endpoints on SageMaker

Practice Exercise

Compare platforms and implement multi-model deployment

Build docs developers (and LLMs) love

Get started for freeTalk to us