Monte Zweben is the CEO and co-founder of Splice Machine. 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.
May 27, 2021 11:35 AM PT
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.
Splice Machine is a hybrid (HTAP) database built upon HBase and Spark. The database powers a one of a kind single-engine feature store, as well as the deployment of ML models as tables inside the database. A simple JDBC connection means Splice Machine can be used with any model ops environment, such as Databricks.
The HBase side allows us to serve features to deployed ML models, and generate ML predictions, in milliseconds. Our unique Spark engine allows us to generate complex training sets, as well as ML predictions on petabytes of data.
In this talk, Monte will discuss how his experience running the AI lab at NASA, and as CEO of Red Pepper, Blue Martini Software and Rocket Fuel, led him to create Splice Machine. Jack will give a quick demonstration of how it all works.
April 23, 2019 05:00 PM PT
Enterprises have been hamstrung in their analytic initiatives by disconnected platforms that were designed to either power applications based on transactional workloads or generate business reports and dashboards using a data warehouse.
With the recent rise of artificial intelligence, companies are now using yet another platform to build predictive and machine learning models. While AI is on the wish list of many companies, enterprises cannot succeed in an environment where business decisions need to be made in real-time using an infrastructure with built-in latency which has been duct taped together using disparate technologies. In this session, we will walk you through how companies can integrate OLTP, OLAP and ML capabilities in a unified platform to make intelligent decisions in real time using data at scale.
We call this Operational AI. By combining an ACID-compliant RDBMS and Data Warehouse with native machine learning, the resulting SQL platform reduces data movement and therefore enables training on real-time data and testing on real-time data leading to better decision-making. Applications benefiting from this approach include fraud detection, precision medicine, supply-chain optimization, preventive maintenance, and personalized marketing. All of these applications benefit materially from being more real-time and data scientists developing these applications can perform more efficient feature engineering leading to faster experimentation. In this talk we show examples powering applications, performing data engineering, and data science all on an integrated platform without requiring any distributed system integration.