Navigating the Complexity of ML Production: Insights and Lessons Learned

Summary:
Our April 5th Speaker, Atul Dhingra, will present his insights into the Machine Learning (ML) model lifecycle in production. The talk covers various aspects of ML Operations that go into making a successful model deployment, from the inception of a problem to making it production ready.

At this presentation you will hear about:

1. Representative Data Distribution
2. Scaling and Deployment
3. Monitoring and Observability
4. MLOps
5. Technical Debt in ML

The talk will also discuss closing the loop between data distribution drift between training and inference time. By the end of the talk, the audience will gain useful insights into how to scale and accelerate the velocity of large production models in a cost-effective way.

About the Speaker:
Atul Dhingra is an Engineering Manager at Standard AI where he works on autonomous checkout powered by Computer Vision and AI.

He has a combined experience of over 10 years in industry and academia working on advanced Machine Learning and Deep Learning algorithms to solve complex problems in the domain of Autonomous checkout, autonomous vehicles, and Biometrics.

He has played a vital role in building large-scale cutting-edge machine-learning production systems.

Agenda:
(Times are Central Time)
6:00pm - brief intros
6:05pm - Presentation by Atul Dhingra
6:45pm - Q&A
7:00 pm - end

Note: You must register with Zoom for the remote broadcast, registering in Meetup does not give you access to the Zoom meeting.