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Course Outline

Containerization Foundations for MLOps

  • Comprehending ML lifecycle requirements
  • Essential Docker concepts for ML systems
  • Best practices for maintaining reproducible environments

Creating Containerized ML Training Pipelines

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

Containerizing Validation and Model Assessment

  • Recreating evaluation environments
  • Automating validation processes
  • Collecting metrics and logs from containers

Containerized Inference and Serving

  • Designing inference microservices
  • Tuning runtime containers for production use
  • Building scalable serving architectures

Orchestrating Pipelines with Docker Compose

  • Managing multi-container ML workflows
  • Isolating environments and managing configuration
  • Integrating auxiliary services (e.g., tracking, storage)

ML Model Versioning and Lifecycle Management

  • Monitoring models, images, and pipeline elements
  • Implementing version-controlled container environments
  • Integrating MLflow or similar tools

Deploying and Scaling ML Workloads

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

CI/CD for MLOps with Docker

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

Overview and Next Steps

Requirements

  • A solid grasp of machine learning workflows
  • Proficiency in Python for data or model development
  • Knowledge of container fundamentals

Target Audience

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

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