Overview

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Deploying a model is only the beginning. Keeping it accurate, cost-effective, and production-ready requires end-to-end MLOps workflows spanning training, monitoring, deployment, and storage. This course builds the skills to operationalize machine learning systems at enterprise scale. You'll fine-tune vision models with Azure ML, track experiments via MLflow, and process streaming IoT telemetry using Azure Stream Analytics. You'll build CI/CD pipelines with GitHub Actions to automate model image builds for AKS, and optimize ML artifact storage using Azure Blob Storage lifecycle policies and feature store architecture. By the end of this course, you'll establish lifecycle standards with drift thresholds and retraining triggers, define experiment tracking practices, set latency targets for streaming data, own CI/CD pipeline reliability, and design storage tiering and feature store strategies balancing cost and performance. Designed for platform engineers adding MLOps expertise to their infrastructure skills. A basic understanding of machine learning concepts and experience with Python are expected.

Syllabus

Fine-Tune & Monitor: Transfer Learning with Azure ML
- This module teaches you to fine-tune pre-trained models using Azure ML's managed training infrastructure. You'll configure compute, run training jobs, and register versioned models for deployment.
Fine-Tune & Monitor: Detect and Respond to Model Drift
- This module teaches you to monitor model performance in production and automate responses to drift. You'll configure drift detection, analyze metrics, and build automated retraining pipelines.
Track & Boost: Experiment Tracking with MLflow
- This module teaches you to implement systematic experiment tracking with MLflow. You'll log training runs, compare results across experiments, and select the best-performing model with documented rationale.
Track & Boost: Feature Engineering for Performance
- This module teaches you to systematically evaluate feature engineering techniques—including normalization, categorical encoding, and embeddings—using experiment tracking. You'll test multiple approaches, measure their impact, and document recommendations for the feature pipeline.
Stream & Backbone: Real-Time Processing with Stream Analytics
- This module teaches you to process streaming data in real time using Azure Stream Analytics. You will write windowed aggregation queries, configure low-latency processing, and validate end-to-end latency meets requirements.
Stream & Backbone: Select a Messaging Platform
- This module teaches you to evaluate and select messaging platforms for high-throughput streaming workloads. You will compare Kinesis, Kafka, and Event Hub across performance and cost dimensions, then produce an evidence-based recommendation.
MLOps Pipelines: Build CI/CD with GitHub Actions
- This module teaches you to build end-to-end continuous integration and continuous delivery (CI/CD) pipelines for machine learning models using GitHub Actions. You will automate image building, registry push, and Kubernetes deployment triggered by code commits.
MLOps Pipelines: Debug and Stabilize Deployments
- This module teaches you to troubleshoot continuous integration and continuous delivery (CI/CD) pipeline failures systematically. You will analyze logs, identify root causes, implement fixes, and track failure rate improvements over time.
Storage & Feature Store: Implement Lifecycle Policies
- This module teaches you to implement automated storage tiering for machine learning (ML) artifacts. You will configure lifecycle policies that move cold data to cheaper tiers, reducing costs while keeping the data accessible for infrequent needs.
Storage & Feature Store: Select Feature Store Database
- This module teaches you to evaluate database options for a feature store. You will compare Azure Cosmos DB and Azure SQL Managed Instance (SQL MI) for low-latency feature serving, measuring performance and cost to produce a recommendation.
Project Module: MLOps Pipeline
- Apply your MLOps skills to design a complete model lifecycle pipeline from training through production monitoring. You'll integrate experiment tracking, CI/CD automation, drift detection, and storage optimization into a cohesive system with documented architecture and operational procedures.