Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Mani Parkhe

Mani Parkhe
ML and AI Platform Engineer, Databricks

Mani Parkhe is an ML and AI platform engineer at Databricks, where he works on various customer-facing and open source platform initiatives to enable data discovery, training, experimentation, and deployment of ML models in the cloud. Mani is a lifelong student and coding geek with a passion for elegance in design. Previously, he spent 15 years building software for semiconductor chip CAD before transitioning to building big data infrastructure, distributed systems and web services, and machine learning. He also worked on various data intensive batch and stream processing problems at LinkedIn and Uber. Mani holds a master’s degree in CS from the University of Florida. He lives in Almaden Valley with his wife and three amazing kids.


2:05pm–2:45pm Wednesday, 09/12/2018
Location: Expo Hall
Secondary topics:  Model lifecycle management
Mani Parkhe (Databricks), Andrew Chen (Databricks)
Successfully building and deploying a machine learning model is difficult to do once. Enabling other data scientists to reproduce your pipeline, compare the results of different versions, track what's running where, and redeploy and rollback updated models is much harder. Mani Parkhe and Andrew Chen offer an overview of MLflow—a new open source project from Databricks that simplifies this process. Read more.