Monte Zweben

CEO and co-founder of Splice Machine @splicemachine

Monte Zweben is the CEO and co-founder of Splice Machine, a provider of real-time machine learning and AI solutions, where he leads the team in their mission to make operational, real-time AI possible for their customers.  

A technology industry veteran, Monte’s early career was spent with the NASA Ames Research Center as the deputy chief of the artificial intelligence branch, where he won the prestigious Space Act Award for his work on the Space Shuttle program. Monte then founded and was the chairman and CEO of Red Pepper Software, a leading supply chain optimization company, which later merged with PeopleSoft, where he was VP and general manager, Manufacturing Business Unit. Then, Monte was the founder and CEO of Blue Martini Software – the leader in e-commerce and omni-channel marketing. Monte is also the co-author of Intelligent Scheduling, and has published articles in the Harvard Business Review and various computer science journals and conference proceedings. He was Chairman of Rocket Fuel Inc. and serves on the Dean’s Advisory Board for Carnegie Mellon University’s School of Computer Science.

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Session

Unified MLOps: Feature Stores and Model Deployment

If you’ve brought two or more ML models into production, you know the struggle that comes from managing multiple data sets, feature engineering pipelines, and models. This talk will propose a whole new approach to MLOps that allows you to successfully scale your models, without increasing latency, by merging a database, a feature store, and machine learning.

Date

Wednesday May 26 / 10:10AM EDT (40 minutes)

Track

State of ML/AI Union

Topics

Machine LearningDevopsContinuous DeliveryContinuous Deployment

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PANEL DISCUSSION

Iterating on Models on Operating ML

What big challenges do we see nowadays in ML?
What do we assume to see in the future?
How can you sort all your ML models and operate them?

Join the discussion with experts actively working on addressing current challenges in building, maintaining, and operating machine learning models. 

Date

Wednesday May 26 / 12:10PM EDT (40 minutes)

Track

State of ML/AI Union

Topics

Machine Learning

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