コース概要

Foundations of Containerization for MLOps

  • Understanding ML lifecycle requirements
  • Key Docker concepts for ML systems
  • Best practices for reproducible environments

Building Containerized ML Training Pipelines

  • Packaging model training code and dependencies
  • Configuring training jobs using Docker images
  • Managing datasets and artifacts in containers

Containerizing Validation and Model Evaluation

  • Reproducing evaluation environments
  • Automating validation workflows
  • Capturing metrics and logs from containers

Containerized Inference and Serving

  • Designing inference microservices
  • Optimizing runtime containers for production
  • Implementing scalable serving architectures

Pipeline Orchestration with Docker Compose

  • Coordinating multi-container ML workflows
  • Environment isolation and configuration management
  • Integrating supporting services (e.g., tracking, storage)

ML Model Versioning and Lifecycle Management

  • Tracking models, images, and pipeline components
  • Version-controlled container environments
  • Integrating MLflow or similar tools

Deploying and Scaling ML Workloads

  • Running pipelines in distributed environments
  • Scaling microservices using Docker-native approaches
  • Monitoring containerized ML systems

CI/CD for MLOps with Docker

  • Automating builds and deployment of ML components
  • Testing pipelines in containerized staging environments
  • Ensuring reproducibility and rollbacks

Summary and Next Steps

要求

  • An understanding of machine learning workflows
  • Experience with Python for data or model development
  • Familiarity with the fundamentals of containers

Audience

  • MLOps engineers
  • DevOps practitioners
  • Data platform teams
 21 時間

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