Production ML 101 - MLOps/LLMOps
Requirements
- Basic understanding of ML algorithms
Description
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.
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.
Who this course is for:
- Beginner who wants to start their journey in ML in Production
- Starting point for Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Data Product Managers, Engineering Leader
Instructor
- 4.3 Instructor Rating
- 23 Reviews
- 1,032 Students
- 1 Course
I have 5+ years of experience after graduating from IIT-B.
I have been passionate about the education sector and I have worked in the leading Edtech sector for more than 2 years managing AI/ML programs with 2400+ learners annually.
As AI/ML space keeps on evolving at a very fast pace, I was responsible for revamping the programs and bringing new technologies into the existing program. Over the past year, I have been involved in creating MLOps and ML Engineering courses.
I want to reach a wider audience and create tangible MLOps outcomes