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How to Use Azure MLOps to Continuously Integrate and Deliver AI Models Streamlining AI Deployment with Azure Machine Learning. This presentation will guide you through leveraging Azure MLOps to build robust, automated, and scalable machine learning workflows. preencoded.png www.visualpath.in +91-7032290546
Introduction to MLOps DevOps for ML Automated Reliability Azure's Role MLOps (Machine Learning Operations) extends DevOps principles to the entire machine learning lifecycle. It enables reliable and automated deployment, management, and monitoring of ML models. Azure MLOps provides the tools and infrastructure to manage, deploy, and monitor models at scale. preencoded.png www.visualpath.in +91-7032290546
Key Components of Azure MLOps Azure Machine Learning: Azure Machine Learning: For model training, deployment, and lifecycle management. Model Registry: Model Registry: Centralized repository for versioning and managing models. Azure DevOps or GitHub Actions: Azure DevOps or GitHub Actions: For robust CI/CD pipelines. Monitoring Tools: Monitoring Tools: Application Insights and Azure Monitor for performance tracking and alerting. ML Pipelines: ML Pipelines: Orchestrate and automate end-to-end ML workflows. Compute Resources: Compute Resources: Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) for scalable inference. preencoded.png www.visualpath.in +91-7032290546
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. preencoded.png www.visualpath.in +91-7032290546
Continuous Integration (CI) Continuous Integration ensures that every code change is automatically built and tested, preventing integration issues early on. Version Control: Version Control: Use Git for robust source code management and experiment tracking, ensuring all changes are logged. Automated Triggers: Automated Triggers: Configure Azure DevOps or GitHub Actions to automatically trigger ML pipeline runs. Pre Pre- -Deployment Validation: Deployment Validation: Implement automated unit testing, data validation, and model quality checks within the CI pipeline. preencoded.png www.visualpath.in +91-7032290546
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 models, with metadata, to the Azure ML Model Registry. Deploy the registered model to staging or production environments using automated scripts. Utilize deployment gates and approval workflows in Azure DevOps for safe and controlled releases. preencoded.png www.visualpath.in +91-7032290546
Model Monitoring and Management Performance Tracking: Performance Tracking: Monitor model performance, data drift, and inference latency using Azure Monitor and Application Insights. Automated Alerts: Automated Alerts: Set up proactive alerts for anomalies, data drift, or performance degradation to ensure timely intervention. Retraining Automation: Retraining Automation: Automatically trigger model retraining pipelines if predefined performance thresholds or data drift metrics are breached, maintaining model relevance. A/B Testing: A/B Testing: Implement A/B testing or canary deployments for new model versions to assess their impact before full rollout. preencoded.png www.visualpath.in +91-7032290546
Benefits of Using Azure MLOps Faster Time to Market Scalable Workflows Accelerate the deployment of AI solutions from development to production. Achieve repeatable and scalable ML workflows across diverse projects. Improved Collaboration Robust Governance Foster seamless collaboration between data scientists and DevOps teams. Ensure comprehensive governance and traceability for all AI models. preencoded.png www.visualpath.in +91-7032290546
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 quality, consistency, and agility in your machine learning deployments. By adopting Azure MLOps, organizations can achieve production AI lifecycle management AI lifecycle management with enhanced efficiency and reliability. production- -grade grade preencoded.png www.visualpath.in +91-7032290546
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