Lifecycle At A Glance
This module follows one practical operating loop all the way through: train → validate → deploy → monitor → retrain. Every section connects back to that loop with Azure-focused examples, debugging paths, and release decisions.
🤖 MLOps — Zero to Hero
Learn how to take machine learning from notebook experiments to reliable production systems: train, validate, deploy, monitor, and retrain with strong version tracking, Azure ML delivery, and Azure DevOps pipeline controls.
Start Learning →16
Lessons
4
Sections
14h
Estimated Time
220+
Interview Q&A
0% complete
🧭
⚙️
DevOps vs MLOps
DevOps moves application code safely. MLOps moves code, data, model artifacts, validation evidence, and live model behavior safely. That difference is why this course emphasizes version lineage, experiment tracking, drift handling, and retraining triggers alongside CI/CD.
🔰 Basics
Start from zero: understand what MLOps is, why the machine learning lifecycle breaks in production, what tooling is required, and how Azure AI services fit into a modern platform.
What is MLOps
Why ML systems need more than a trained model: operational lifecycle, collaboration, automation, and production reliability.
Lesson 1
ML Lifecycle from Training to Monitoring
Understand the end-to-end flow: train, validate, register, deploy, monitor, and retrain.
Lesson 2
MLOps Environments, Reproducibility, and Tooling
Dev/test/prod separation, environment pinning, Docker, MLflow, Git, and reproducible execution.
Lesson 3
Azure ML and Azure AI Foundations for MLOps
Azure Machine Learning workspaces, registries, endpoints, and where Azure OpenAI and Azure AI services complement the stack.
Lesson 4
⚙️ Intermediate
Move into delivery mechanics: version data and models, track experiments, package artifacts, and deploy models with safe release patterns.
Data and Model Versioning
Track which dataset, code, hyperparameters, and model artifact produced each production prediction.
Lesson 5
Training, Validation, and Experiment Tracking
Run repeatable experiments, compare runs, validate quality gates, and decide when a model is deployable.
Lesson 6
Packaging and Deploying Models
Build inference artifacts, containerize serving code, and publish models to managed endpoints.
Lesson 7
Model Serving Patterns and Release Strategies
Batch vs real-time inference, canary releases, shadow deployments, blue-green rollouts, and rollback logic.
Lesson 8
🚀 Advanced
Operate MLOps like a platform: automate CI/CD, monitor drift and latency, build retraining loops, and govern models responsibly.
CI/CD for Machine Learning Models
Integrate Git, tests, model validation, approvals, and automated promotion through Azure DevOps pipelines.
Lesson 9
Model Monitoring, Drift, and Observability
Watch for performance decay, data drift, latency issues, failed predictions, and business KPI regressions.
Lesson 10
Retraining Pipelines and Feedback Loops
Design safe retraining triggers, dataset refresh pipelines, approval gates, and model replacement logic.
Lesson 11
Governance, Security, and Responsible MLOps
Secure artifacts, protect secrets, enforce approvals, track lineage, and handle compliance and bias concerns.
Lesson 12
🧪 Hands-on Labs
Practice with realistic enterprise labs, Azure ML pipelines, deployment failures, rollback scenarios, and interview-style production reasoning.
Lab: Build an Azure ML Training Pipeline
Create a reproducible training pipeline with code, environment, dataset, and experiment tracking in Azure ML.
Lesson 13
Lab: Deploy a Model with Azure DevOps and Azure ML
Package a model, run validation gates, and deploy to a managed online endpoint using an Azure DevOps release pipeline.
Lesson 14
Debugging MLOps Pipeline and Deployment Failures
Fix missing dependencies, bad model signatures, endpoint errors, dataset mismatches, and broken promotion pipelines.
Lesson 15
Interview Preparation - MLOps
Beginner, intermediate, and scenario-based MLOps questions with practical answers about lifecycle, drift, CI/CD, and governance.
Lesson 16