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 Duration 14 hours

Course Outline

MLOps Foundations on Kubernetes

  • Essential MLOps concepts
  • Distinguishing MLOps from traditional DevOps
  • Addressing key challenges in ML lifecycle management

Containerization of ML Workloads

  • Packaging models alongside training code
  • Optimizing container images specifically for ML tasks
  • Handling dependencies to ensure reproducibility

Implementing CI/CD for Machine Learning

  • Structuring ML repositories to support automation
  • Incorporating testing and validation stages
  • Automating pipeline triggers for retraining and updates

GitOps in Model Deployment

  • Principles and workflows of GitOps
  • Leveraging Argo CD for deploying models
  • Managing version control for models and configurations

Orchestrating Pipelines on Kubernetes

  • Constructing pipelines using Tekton
  • Overseeing multi-step ML workflows
  • Handling scheduling and resource allocation

Monitoring, Logging, and Rollback Tactics

  • Monitoring data drift and model performance
  • Integrating alerting systems and observability tools
  • Implementing rollback and failover procedures

Automated Retraining and Continuous Enhancement

  • Establishing effective feedback loops
  • Automating scheduled retraining cycles
  • Utilizing MLflow for tracking and managing experiments

Advanced MLOps Architectures

  • Multi-cluster and hybrid-cloud deployment strategies
  • Enabling team scaling via shared infrastructure
  • Addressing security and compliance requirements

Recap and Future Directions

Requirements

  • A solid grasp of Kubernetes fundamentals
  • Practical experience with machine learning workflows
  • Familiarity with Git-based development practices

Target Audience

  • ML engineers
  • DevOps engineers
  • ML platform teams

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