MLOps fundamentals of Continuous Integration & Continuous Delivery (CI/CD) using Azure DevOps & Azure Machine Learning
What you'll learn
- Basics of MLOps, benefits and its implementation.
- Challenges faced by teams in the current way of handling Machine learning projects.
- Importance of MLOps principles in Machine learning projects.
- Standards and principles followed in MLOps culture.
- What is continuous integration, continuous delivery and continuous training in MLOps space.
- Various maturity levels associated with MLOps.
- MLOps tools stack and various MLOps platforms comparison.
- Run an end-to-end CI/CD MLOps pipeline using Azure DevOps & Azure Machine learning.
Description
Important Note: The intention of this course is to teach MLOps fundamentals and not Azure ML. Azure demo section is included as a proof to show the working of an end-to-end MLOps project. All the codes involved in pipeline are explained though."MLOps is a culture with set of principles, guidelines defined in machine learning world for smooth implementation and productionization of Machine learning models."
Data scientists have been experimenting with machine learning models from 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 an non systemization in ML lifecycle, 80% of the models never make it to production and remain stagnated as an academic experiment only.
Machine Learning Operations (MLOps), emerged as a solution to the problem, is a new culture in the market and a rapidly growing space that encompasses everything required to deploy a machine learning model into production.
As per the tech talks in 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.
Who this course is for:
- Data scientists
- Data engineers
- ML engineers
- Devops engineers
