MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo

Important Note: The intention of this course is to teach MLOps fundamentals and not Azure ML. Azure demo section is included as proof to show the working of an end-to-end MLOps project. All the codes involved in the pipeline are explained though.

Data scientists have been experimenting with machine learning models for a long time, but to provide the real business value, they must be operationalized i.e. push the models to production. Unfortunately, due to the current challenges and a non-systemization in ML lifecycle, 80% of the models never make it to production and remain stagnated as an academic experiment only.

As per the tech talks in the market, 2021 is the year of MLOps and would become the mandate skill set for Enterprise ML projects.

What’s included in the course?

• MLOps core basics and fundamentals.
• What were the challenges in the traditional machine learning lifecycle management?
• How MLOps is addressing those issues while providing more flexibility and automation in the ML process.
• Standards and principles on which MLOps is based upon.
• Continuous integration (CI), Continuous Delivery (CD), and Continuous training (CT) pipelines in MLOps.
• Various maturity levels associated with MLOps.
• MLOps tools stack and MLOps platforms comparisons.
• Quick crash course on Azure Machine learning components.
• An end-to-end CI/CD MLOps pipeline for a case study in Azure using Azure DevOps & Azure Machine learning.