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πŸ”„ MLOps

Cover experiment tracking, feature pipelines, model registry, ML delivery, scalable serving, monitoring and controlled releases.

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Overview

This MLOps specialist course follows the model lifecycle through experiment tracking, data validation, feature pipelines, versioning, CI/CD, serving, Kubeflow pipelines, monitoring, drift detection and staged deployment patterns.

What you will learn

  • MLOps Foundations
  • Experiment Tracking
  • Data Validation & Feature Pipelines
  • Model Registry & Versioning
  • CI/CD for Machine Learning
  • Model Serving & Scalable Inference

Tools & technologies

πŸ”„MLOpsπŸ§ͺExperiment TrackingπŸ—‚οΈModel RegistryπŸ—οΈKubeflowπŸš€Model ServingπŸ“‰Drift Detection

Syllabus

  • MLOps Foundations
  • Experiment Tracking
  • Data Validation & Feature Pipelines
  • Model Registry & Versioning
  • CI/CD for Machine Learning
  • Model Serving & Scalable Inference
  • ML Pipelines with Kubeflow
  • Model Monitoring & Drift Detection
  • A/B Testing & Canary Deployments
  • Platform Engineering & Capstone
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