MLOps, Data Drift and Concept Drift in Production

Modern applications have AI at its core. The next presumable years, engineers across the stack will work on different aspects of taking AI into production. The field of MLOps which is an intersection of data engineering, DevOps and data science will be the norm in engineering teams.

In this talk, Nischal will talk about his personal experience in the last 5 years in the field of data science and MLOps. He will also talk about data science evaluation and the importance of data drift and concept drift in production for machine learning models.

What to expect to learn from this session?

1. Uncertainty cannot be eliminated in the performance of the model. So extensive testing, circuit breakers, fallback mechanisms, and incremental deployment mechanisms are critical.
2. ML development and deployment should be thought about within the context of the product development.
3. Many of the systems, methods and ideas are applicable here including versioning but need to be repurposed to suit the context

Speaker Bio:
Nischal Harohalli Padmanabha
VP, Technology - Data Engineering and Data Science at Omnius
Over the last decade, he has had the unique experience of being part of teams that have used data science to solve real-world problems.
The last few years he has been ideating and architecting an AI/ML platform with a pure focus on MLOps for the enterprise world using open source technologies with a focus on building cross-functional teams, scaling release process engineering, and ensuring the reliability of AI and engineering systems in production at scale.