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We're here to help you find itAzure MLOps Course Overview
The Azure MLOps course is designed to educate learners on how to effectively implement machine learning operations within the Azure cloud environment. This comprehensive course covers the full spectrum of MLOps activities, from setting up Azure ML workspaces to deploying and maintaining machine learning models.
Module 1 kicks off the course by explaining what Azure ML Ops is, its necessity, and provides an overview of the workflow. In Module 2, learners set up the environment, including integration with Azure DevOps and GitHub. Module 3 dives into creating and running experiments, while Module 4 focuses on pipelines for seamless ML workflows. Collaboration and version control are covered in Module 5, ensuring teams can work effectively on ML projects. Module 6 teaches model deployment, including deploying to AKS, and Module 7 addresses the monitoring and maintenance of these deployments.
Upon completion, learners can aim for Azure MLOps certification, solidifying their grasp of best practices and key concepts. The final Module 8 wraps up the course and guides learners on implementing MLOps strategies within their organizations, ensuring they can apply their knowledge practically.
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Classroom Training price is on request
To ensure you have the best learning experience in the Azure MLOps course, it's important to come prepared with a certain foundation of knowledge and skills. Below are the minimum required prerequisites for successfully undertaking this training:
These prerequisites are designed to ensure that you can actively engage with the course content and fully benefit from the Azure MLOps training. If you feel you need to strengthen any of these areas, Koenig Solutions offers foundational courses that can help you prepare for the Azure MLOps course.
Azure MLOps course by Koenig Solutions focuses on integrating machine learning workflows with Azure DevOps for streamlined deployment and management.
Target Audience for the Azure MLOps Course:
Gain proficiency in Azure ML Ops to streamline your machine learning workflow, from experiment management and version control to model deployment and monitoring, ensuring repeatable and scalable ML processes.