SageMarker

TO-DO

• Model Monitoring?
• Model Deployment
  • Endpoints: Deploy trained models to fully managed endpoints for real-time predictions.
  • Multi-Model Endpoints: Serve multiple models from a single endpoint, optimizing cost and management overhead.
  • Serverless Inference: Deploy models without managing infrastructure, perfect for intermittent or unpredictable traffic patterns.

Machine learning workflow

Purpose:

• Specifically designed for machine learning (ML) workflows.
• Integrates deeply with SageMaker and other AWS ML services.

Features:

• Built-in ML Components: Pre-built steps for common ML tasks (e.g., data processing, training, model evaluation).
• Pipeline Structure: Define ML workflows using a series of steps, such as data preprocessing, training, and model deployment.
• Versioning: Tracks versions of pipelines, steps, and artifacts.
• Automation: Automates end-to-end ML workflows, from data ingestion to model deployment.
• Integration with SageMaker: Seamless integration with SageMaker's training, tuning, and hosting services.
• Optimization: Built to optimize ML-specific tasks, providing easy management and monitoring of ML experiments.

Example Use Case:

• Automating the entire ML lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment within the SageMaker environment.

AWS Step Functions

Purpose:

• General-purpose orchestration service for coordinating the components of distributed applications and microservices.
• Not limited to ML workflows.

Features:

• Flexibility: Can coordinate any AWS service, custom service, or third-party service.
• State Machines: Define workflows as state machines with states like Pass, Fail, Wait, Choice, Parallel, and Map.
• Error Handling: Built-in error handling, retry logic, and conditional branching.
• Integration: Integrates with a wide range of AWS services, including Lambda, ECS, DynamoDB, SNS, SQS, and more.
• Visual Workflow: Provides a visual interface to design and monitor workflows.

Example Use Case:

• Orchestrating complex workflows that might include data processing, invoking APIs, managing data pipelines, and integrating multiple AWS services.

Key Differences

Feature	SageMaker Pipelines	AWS Step Functions
Primary Focus	Machine Learning	General-purpose workflow orchestration
Integration	Deep integration with SageMaker services	Broad integration across AWS services
Pre-built Steps	ML-specific steps (e.g., training, tuning)	Generic steps for various use cases
Workflow Definition	Step-based ML workflows	State machine-based workflows
Error Handling & Branching	Basic support	Advanced error handling and conditional branching
Versioning	Yes, with focus on ML artifacts	No inherent versioning for workflows
Use Cases	End-to-end ML lifecycle automation	Orchestration of diverse applications and services

Choosing the Right Tool

• Use SageMaker Pipelines if:

  • You are focused on building and automating ML workflows.
  • You need tight integration with SageMaker's ML capabilities.
  • You want built-in components for common ML tasks.

• Use AWS Step Functions if:

  • You need to orchestrate a variety of AWS services or custom services.
  • Your workflow includes non-ML tasks or services.
  • You require advanced workflow control with error handling and branching.

Both services can be used together in scenarios where you need the specific ML capabilities of SageMaker Pipelines within a broader application workflow managed by Step Functions.

Example Diagrams

SageMaker Pipelines

AWS Step Functions

These diagrams illustrate how each tool can be used to define a workflow, with SageMaker Pipelines focused on ML steps and Step Functions providing a more flexible orchestration capability.