Azure MLOps and Machine Learning DevOps Course

This Azure MLOps and Machine Learning DevOps course is designed for engineers and data scientists who want to take machine learning models from development to production using Microsoft Azure. The course focuses on building scalable, automated, and production-ready machine learning workflows using Azure Machine Learning Service, Azure DevOps, and Databricks.

Learners gain practical experience in building full ML pipelines that include training, deployment, monitoring, and lifecycle management in real enterprise environments.

1.1 Introduction to Azure MLOps and DevOps for ML

This section introduces the concept of MLOps and explains how DevOps practices are applied to machine learning systems. Learners understand the importance of automation, collaboration, and reproducibility in production ML workflows.

It also explains how Azure tools support end-to-end machine learning lifecycle management.

1.1.1 What is MLOps?

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This part explains MLOps as the combination of machine learning and DevOps, focusing on automation, monitoring, and continuous improvement of ML systems.

1.1.2 ML Lifecycle in Production Systems

Here learners explore the stages of a machine learning system from data ingestion and training to deployment and monitoring.

1.2 Training Models with Azure Machine Learning Service

This section focuses on building and training machine learning models using Azure Machine Learning Service in a structured and production-ready way.

Learners also understand how to organize ML projects for scalability and collaboration.

1.2.1 Structuring ML Projects for Production

This topic explains how to design machine learning project structures that are maintainable and suitable for enterprise deployment.

1.2.2 Model Training in Azure ML Service

Here learners explore how to train models using Azure ML compute resources and managed environments.

1.3 CI/CD for Machine Learning Systems

This section introduces Continuous Integration and Continuous Deployment (CI/CD) practices applied to machine learning workflows.

Learners understand how automation improves reliability and speed of ML systems.

1.3.1 Building ML CI/CD Pipelines

This topic explains how to automate training, testing, and deployment processes using Azure DevOps pipelines.

1.3.2 Model Versioning and Lifecycle Management

Here learners explore how different versions of models are tracked and managed throughout development and production stages.

1.4 Model Deployment Strategies in Azure

This section focuses on deploying machine learning models into different environments depending on scalability and performance needs.

1.4.1 Deployment using Azure Container Instances (ACI)

This part explains how lightweight models are deployed for quick testing and low-scale production use.

1.4.2 Scalable Deployment using Azure Kubernetes Service (AKS)

Here learners understand how to deploy machine learning models in scalable, production-grade Kubernetes environments.

1.5 Databricks and MLflow Integration

This section introduces Databricks and MLflow for experiment tracking, model management, and improving reproducibility in machine learning workflows.

1.5.1 Experiment Tracking with MLflow

This topic explains how MLflow tracks experiments, logs metrics, and compares model performance.

1.5.2 Model Management and Reproducibility

Here learners explore how to manage model versions and ensure consistent results across experiments.

1.6 Final Skills and Learning Outcomes

By the end of this course, learners will be able to design, build, and deploy complete MLOps pipelines using Azure Machine Learning, Azure DevOps, and Databricks.

They will gain the ability to automate machine learning workflows, manage models in production, and implement scalable AI systems in real-world environments.