Are you confused with so many tools out there in MLOps? Are you confused where to start your journey in MLOps?

What you will learn

Understand the approach to ML to Production

Understand the fundamentals of MLOps in Production

Understand MLOps as a process – From Business Discussions – ML in Production

Evaluation of different types of tools – Make sense of plethora of tools

Understand different job roles and their future roadmaps


Are you looking to start your journey in ML in production? Are you confused with so many tools? Are you confused about where to start your journey?

Did you know >50% of people discontinue their journey in ML in production because they feel overwhelmed.

Our comprehensive course on MLOps in production is designed to help you do just that to teach you the proper approach to ML in production.

According to the BCGs report, the pioneers of AI @ scale—the companies that have scaled AI across the business and achieved meaningful value from their investments—typically dedicate 10% of their AI investment to algorithms, 20% to technologies, and 70% to embedding AI into business processes and agile ways of working.

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Why give so much importance to the tools? Rather emphasis should be given to the process.

This course is suitable for anyone looking to advance their machine learning skills, including Data engineers, ML engineers, Data Scientists, MLOps platform engineers, and MLOps Engineers. By the end of the course, you’ll have a deep understanding of the major root causes of failure in ML in production, the fundamentals of MLOps, MLOps as a process and the future roadmap in ML in production.

I have been working along with industry experts and industry mentors for the past year to understand the root causes in ML in production.



Fundamentals of ML in Production

Course Introduction
Root Cause of Failure
Fundamentals – ML System vs ML Code
Fundamentals – ML Research vs ML Production
Fundamentals – DevOps vs MLOps
Fundamentals – MLOps vs LLMOps

Making sense of tooling ecosystem

MLOps as a mess – plethora of tools
Making sense of the mess – End to End MLOps Lifecycle
Making sense of the mess – Tooling Ecosystem
Making sense of the mess – Levels of frameworks

Future prospects in Production ML

Industry of Production ML
Career Prospects in Production ML