Getting Started with Azure ML MLOps

This section introduces the core concepts of MLOps and how Azure Machine Learning facilitates these practices. Understand the benefits of a robust MLOps pipeline for your machine learning projects.

End-to-End MLOps Pipeline Setup

Learn how to set up a complete MLOps pipeline from data ingestion to model deployment and monitoring using Azure DevOps and Azure Machine Learning.

Azure DevOps Azure ML CI/CD Pipelines
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Model Training and Versioning

Discover best practices for training machine learning models, tracking experiments, and managing model versions effectively within Azure ML.

Model Training Experiment Tracking Model Registry
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Automated Model Deployment

Implement automated deployment strategies for your trained models to various targets, including Azure Kubernetes Service and Azure Container Instances.

Deployment AKS ACI CI/CD
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Advanced MLOps Techniques

Dive deeper into advanced MLOps scenarios, including model monitoring, retraining, and governance.

Model Monitoring and Performance Tracking

Learn how to monitor deployed models for data drift, model degradation, and performance metrics, ensuring your models remain effective.

Model Monitoring Data Drift Performance Metrics
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Automated Retraining and Deployment

Set up triggers for automatic model retraining based on monitoring alerts and redeploy updated models seamlessly.

Retraining Automation CI/CD
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ML Governance and Compliance

Understand how to implement governance policies, audit trails, and ensure compliance for your machine learning workflows in Azure.

Governance Compliance Auditing
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