The process of designing and deploying ML algorithms extends much beyond the the data science element. Multiple teams, staffed with people with a variety of skills, who use different tools, are involved.
And hence, it is imperative for data scientists to understand the process from the viewpoint of these roles, responsibilities and tasks.
MLOps helps provide that view.
With a good understanding of MLOps, data scientists can gauge the complexity of the process beyond the data science elements, understand the challenges of other teams involved, and hence can help collaborate better.
After all, what everyone wants eventually is the successful deployment of a robust and useful ML model.
We introduce a high-level overview of MLOps in this episode of “Edge AI Bytes”.
Kumar Singh is the founder of Designed Analytics LLC, which is focused on helping organizations explore how to leverage data and analytics to effectively compete, thrive and innovate.
Kumar has over a decade of hands-on experience in supply chain and operations analytics. With over a decade of industry experience, he has worked across multiple industries, helping companies set up analytics centers of excellence.
Post his industry experience, Kumar also did a stint in external consulting as a data science consultant with Boston Consulting Group (BCG). Kumar holds an MSc. in AI from Liverpool John Moores University, a Masters in Supply Chain from The Ross School of Business at The University of Michigan, Ann Arbor, an MBA in Operations Management from IIT Roorkee, India, and an undergraduate degree in Electrical engineering.
Kumar is ASCM CPIM, CSCP, and CLTD certified and holds the PMP certification from PMI. He is also an AWS-certified Machine Learning Specialist and Microsoft certified in the Azure IoT developer specialty.
Designed Analytics Blog 