Abstract

In real world, production ML systems is a complex process of which building the model is just a small part. Alarmingly less than 20% of the total models built make it to production. The real challenge comes in when these models must be deployed and maintained in production to generate real business value. We will delve into the groundbreaking world of MLOps, where the fusion of Machine Learning (ML) and Operations (Ops) is transforming the way organizations approach the development and deployment of productiongrade ML pipelines. With a keen focus on the end-to-end lifecycle of ML models, the talk will provide an in-depth exploration of various components critical for successful MLOps implementation in the industry. Emphasizing the significance of seamless model training, robust deployment strategies, and real-time drift detection mechanisms, attendees will gain valuable insights into best practices and tools used in ensuring model consistency and reliability.

Bio.

Satyajit Padhy received his Ph.D. degree with the Institute of Information Systems and Applications, National Tsing Hua University, Taiwan. His PhD research focused on resource management algorithms in Network Function Virtualization. His industry experience started with Industrial Technology Research Institute (ITRI),Taiwan where he was the lead engineer in developing an Auto Machine Learning (ML) system & building ML model for edge devices. He joined Micron Technology as a Senior ML engineer in 2021 where he is responsible in building and maintaining cost-friendly, production ML pipelines.

All faculty and students are welcome to join.