Microsoft Azure | Top Azure AI Engineer Training in Ameerpet
                     How to Use Azure 
MLOps to Continuously 
Integrate and Deliver 
SAtreIam lMiningo AI Ddepeloymlesnt with Azure Machine Learning. This 
presentation will guide you through leveraging Azure MLOps to 
build robust, automated, and scalable machine learning workflows.
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Introduction to MLOps
DevOps for ML Automated Reliability Azure's Role
MLOps (Machine Learning It enables reliable and Azure MLOps provides the tools 
Operations) extends DevOps automated deployment, and infrastructure to manage, 
principles to the entire machine management, and monitoring of deploy, and monitor models at 
learning lifecycle. ML models. scale.
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Key Components of Azure MLOps
• Azure Machine Learning: For model training, • Model Registry: Centralized repository for 
deployment, and lifecycle management. versioning and managing models.
• Azure DevOps or GitHub Actions: For robust • Monitoring Tools: Application Insights and Azure 
CI/CD pipelines. Monitor for performance tracking and alerting.
• ML Pipelines: Orchestrate and automate end-to- • Compute Resources: Azure Kubernetes Service 
end ML workflows. (AKS) or Azure Container Instances (ACI) for scalable 
inference.
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MLOps Workflow Overview
Data Ingestion
Automated collection and preparation of data.
Model Development
Experimentation, training, and versioning of models.
Model Validation
Rigorous testing and quality assurance of models.
Model Registration
Storing and managing validated models in a central registry.
Model Deployment
Deploying models to various environments (staging, production) for inference.
Monitoring & Feedback
Tracking model performance, data drift, and continuous feedback loops.
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Continuous Integration (CI)
Continuous Integration ensures that every code change is automatically built and tested, preventing integration issues early on.
• Version Control: Use Git for robust source code management and 
experiment tracking, ensuring all changes are logged.
• Automated Triggers: Configure Azure DevOps or GitHub Actions to 
automatically trigger ML pipeline runs.
• Pre-Deployment Validation: Implement automated unit testing, data 
validation, and model quality checks within the CI pipeline.
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Continuous Delivery (CD)
Continuous Delivery ensures that validated models are automatically prepared for release, ready for deployment to 
any environment.
1 2 3
Model Registration Environment Deployment Safe Release Gates
Automatically register validated Deploy the registered model to Utilize deployment gates and 
models, with metadata, to the staging or production approval workflows in Azure 
Azure ML Model Registry. environments using automated DevOps for safe and controlled 
scripts. releases.
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Model Monitoring and Management
• Performance Tracking: Monitor model performance, data drift, 
and inference latency using Azure Monitor and Application 
• AInusitgohmtsa.ted Alerts: Set up proactive alerts for anomalies, data 
drift, or performance degradation to ensure timely intervention.
• Retraining Automation: Automatically trigger model 
retraining pipelines if predefined performance thresholds or data 
drift metrics are breached, maintaining model relevance.
• A/B Testing: Implement A/B testing or canary deployments for 
new model versions to assess their impact before full rollout.
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Benefits of Using Azure MLOps
Faster Time to Market Scalable Workflows
Accelerate the deployment of AI solutions from Achieve repeatable and scalable ML workflows across 
development to production. diverse projects.
Improved Collaboration Robust Governance
Foster seamless collaboration between data scientists Ensure comprehensive governance and traceability for 
and DevOps teams. all AI models.
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Conclusion
Azure MLOps provides a comprehensive framework to simplify and 
optimize the continuous integration and delivery of AI models.
• It ensures quality, consistency, and agility in your machine 
learning deployments.
• By adopting Azure MLOps, organizations can achieve 
production-grade AI lifecycle management with enhanced 
efficiency and reliability.
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For More Information About 
  Azure AI
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Hyderabad-16
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Thank You
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