MLOps on Azure: From Data Science to Deployment

The MLOps on Azure: From Data Science to Deployment course provides a comprehensive guide to implementing Machine Learning Operations (MLOps) on Microsoft Azure. This course covers end-to-end ML model lifecycle management, from model development and version control to automated deployment, monitoring, and scaling using Azure Machine Learning, Azure DevOps, and CI/CD pipelines.

Who Should Enroll Now?

• Data Scientists looking to streamline ML workflows.
• ML Engineers deploying scalable machine learning models.
• DevOps Professionals integrating AI models into production environments.
• Cloud & AI Architects optimizing ML model performance on Azure.

Key Learning Objectives:

• Understand MLOps principles and best practices.
• Automate model training, validation, and deployment using Azure ML.
• Implement CI/CD pipelines for ML workflows with Azure DevOps.
• Monitor model performance, drift, and retraining strategies.
• Optimize ML model performance using Azure Kubernetes Service (AKS) and Azure Functions.

Why Choose This Course?
✅ Hands-on experience with MLOps automation on Azure.
✅ Covers model versioning, reproducibility, and scaling.
✅ Learn to deploy, monitor, and manage ML models efficiently.
✅ Gain practical knowledge to operationalize AI solutions.