Azure MLOps from Microsoft

Basic knowledge of machine learning concepts and familiarity with Azure services are recommended. Some experience with Azure DevOps and GitHub will be beneficial but not mandatory.

The course is divided into eight modules:

• Module 1: Introduction to Azure MLOps – Overview of MLOps and its workflow.
• Module 2: Environment Setup – Setting up Azure ML workspaces and integrating with Azure DevOps and GitHub.
• Module 3: Experimentation – Creating and running machine learning experiments.
• Module 4: Building Pipelines – Developing and managing ML pipelines.
• Module 5: Collaboration and Version Control – Best practices for team collaboration and version control.
• Module 6: Model Deployment – Deploying models, including to Azure Kubernetes Service (AKS).
• Module 7: Monitoring and Maintenance – Monitoring and maintaining model deployments.
• Module 8: Implementing MLOps Strategies – Applying MLOps strategies within organizations and preparing for Azure MLOps certification.

You will learn how to set up and manage Azure ML workspaces, create and run ML experiments, build and manage ML pipelines, collaborate effectively on ML projects, deploy models, and monitor and maintain deployments.

Yes, the course includes practical hands-on labs and projects that allow you to apply the concepts learned in real-world scenarios.

Azure MLOps is beneficial across various industries, including finance, healthcare, retail, and technology. It helps organizations manage and operationalize machine learning models efficiently, leading to improved decision-making and business outcomes.

You can enroll by visiting the CounselTrain website and following the registration process. For further assistance, you can contact our support team.