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Course Outline

Foundations of MLOps on Kubernetes

  • Essential concepts within MLOps.
  • Differentiating MLOps from traditional DevOps.
  • Addressing key challenges in ML lifecycle management.

Containerizing ML Workloads

  • Packaging models alongside training code.
  • Optimizing container images for ML efficiency.
  • Managing dependencies to ensure reproducibility.

CI/CD for Machine Learning

  • Organizing ML repositories to facilitate automation.
  • Embedding testing and validation steps into the pipeline.
  • Automating triggers for retraining and model updates.

GitOps for Model Deployment

  • Applying GitOps principles and workflows.
  • Leveraging Argo CD for deploying models.
  • Implementing version control for models and configurations.

Pipeline Orchestration on Kubernetes

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

Monitoring, Logging, and Rollback Strategies

  • Monitoring data drift and model performance metrics.
  • Enhancing alerting and observability integrations.
  • Implementing rollback and failover procedures.

Automated Retraining and Continuous Improvement

  • Creating effective feedback loops.
  • Automating scheduled retraining processes.
  • Integrating MLflow for tracking and experiment management.

Advanced MLOps Architectures

  • Deploying models across multi-cluster and hybrid-cloud environments.
  • Scaling team capabilities with shared infrastructure.
  • Navigating security and compliance requirements.

Summary and Next Steps

Requirements

  • Familiarity with fundamental Kubernetes concepts.
  • Practical experience with machine learning workflows.
  • Proficiency in Git-based development practices.

Target Audience

  • Machine Learning Engineers.
  • DevOps Engineers.
  • ML Platform Teams.
 14 Hours

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