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Course Outline

Containerization Foundations for MLOps

  • Examining ML lifecycle requirements
  • Essential Docker concepts for ML systems
  • Best practices for establishing reproducible environments

Creating Containerized ML Training Pipelines

  • Packaging model training code along with dependencies
  • Setting up training jobs via Docker images
  • Handling datasets and artifacts within containers

Containerization of Validation and Model Evaluation

  • Ensuring reproducibility in evaluation environments
  • Automating validation processes
  • Capturing metrics and logs from containerized instances

Containerized Inference and Serving

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

Orchestrating Pipelines with Docker Compose

  • Coordinating multi-container ML workflows
  • Managing environment isolation and configuration
  • Integrating auxiliary services (e.g., tracking, storage)

ML Model Versioning and Lifecycle Management

  • Tracking models, images, and pipeline elements
  • Utilizing version-controlled container environments
  • Integrating with MLflow or comparable tools

Deployment and Scaling of ML Workloads

  • Executing pipelines in distributed environments
  • Scaling microservices using native Docker methods
  • Monitoring containerized ML systems

Implementing CI/CD for MLOps with Docker

  • Automating the build and deployment of ML components
  • Testing pipelines in containerized staging environments
  • Safeguarding reproducibility and rollback capabilities

Summary and Next Steps

Requirements

  • A solid understanding of machine learning workflows
  • Proficiency in Python for data or model development
  • Familiarity with container fundamentals

Target Audience

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

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