Machine Learning Operations (MLOps)
Learn Machine Learning Operations (MLOps) with practical skills in ML pipelines, experiment tracking, model deployment, monitoring, and production ML systems.
Course Overview
Course Modules Module 1: MLOps Fundamentals MLOps Principles ML Lifecycle Management Model Registry Versioning Data & Models ML Platforms Overview MLOps Maturity Model Module 2: Experiment Tracking MLflow Tracking Weights & Biases Experiment Management Hyperparameter Tuning Model Comparison Reproducibility Module 3: Model Serving & Deployment REST API Serving FastAPI & Flask Docker Containerization Kubernetes Deployment BentoML & Seldon Canary & Blue-Green Deployments Module 4: Feature Stores & Data Management Feature Store Concepts Feast Framework Data Versioning (DVC) Data Pipelines Training Data Management Feature Engineering at Scale Module 5: Model Monitoring & Governance Data Drift Detection Model Performance Monitoring Evidently AI Model Explainability Fairness & Bias Auditing Compliance & Governance Module 6: Capstone Project End-to-End ML Pipeline Experiment Tracking Model Deployment Monitoring Setup Retraining Automation Documentation Capstone Project Build a full MLOps pipeline from data versioning and experiment tracking to automated model deployment and monitoring.
Learning Outcomes
Design and implement end-to-end MLOps pipelines.
Manage ML experiments with MLflow and Weights & Biases.
Deploy ML models using Docker, Kubernetes, and APIs.
Monitor model drift and performance in production.
Apply ML governance and model explainability practices.
Prerequisites
Participants should have: Strong machine learning background Python and cloud computing experience Familiarity with Docker and CI/CD concepts
Enterprise Learning
Tailored learning experiences for enterprises, institutions, and professional cohorts.