Splice Machine to Present on Machine Learning and Operational AI at Spark + AI Summit 2019
April 22, 2019
Founder and CEO, Monte Zweben, and co-founder and vice president, Gene Davis, will discuss operational AI and Splice Machine’s new data science workbench, ML Manager
SAN FRANCISCO, April 22, 2019 – Splice Machine, the first and only operational artificial intelligence (AI) data platform, today announced that its founder and CEO, Monte Zweben, and its co-founder and vice president of product development, Gene Davis, will discuss operational AI and the company’s new ML Manager at the Spark + AI Summit 2019 being held April 23-25, 2019 in San Francisco, CA. Splice Machine will also showcase ML Manager at Booth 414 at the conference.
“Today’s enterprises cannot succeed when business decisions that need to be made in real-time are made too late and on stale data because the infrastructure has been duct-taped together using disparate technologies,” said Zweben. “We look forward to showing Spark + AI Summit attendees how an operational AI platform can empower them to leverage real-time data for better business outcomes from their mission-critical applications.”
Without Operational AI, Your ML Model Is Stale
Monte Zweben, Co-Founder and CEO, Splice Machine
Wednesday, April 24, 2019
5:20 – 5:35 pm PDT
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 AI, companies are now using yet another platform to build predictive and machine learning models.
This session will focus on how companies can achieve operational AI by integrating OLTP, OLAP and ML capabilities on a unified platform to make intelligent decisions in real time using data at scale. By combining an ACID-compliant RDBMS and Data Warehouse with native machine learning, the resulting SQL platform reduces data movement and, therefore, enables training 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, Monte Zweben will share examples of powering applications, performing data engineering, and data science all on an integrated platform without requiring any distributed system integration.
Splice Machine’s Use of Apache Spark™ and MLflowGene Davis, Co-Founder and Vice President, Product Management, Splice Machine
Thursday, April 25, 2019
5:30 – 6:10 pm PDT
Gene Davis will demonstrate Splice Machine’s data science workbench, ML Manager, and how it leverages Apache Spark and MLflow to create powerful, full-cycle machine learning capabilities on an integrated platform, from transactional updates to data wrangling, experimentation and deployment, and back again.
Splice Machine is an ANSI-SQL Relational Database Management System (RDBMS) on Apache Spark. It has proven low-latency transactional processing (OLTP), as well as analytical processing (OLAP) at petabyte scale. It uses Apache Spark for all analytical computations and leverages HBase for persistence.
This talk highlights a new Native Spark Datasource – which enables seamless data movement between Spark Data Frames and Splice Machine tables without serialization and deserialization. This Spark Datasource makes machine learning libraries such as MLlib native to the Splice RDBMS. Splice Machine has now integrated MLflow into its data platform, creating a flexible Data Science Workbench with an RDBMS at its core. The transactional capabilities of Splice Machine, integrated with a large number of DataFrame-compatible libraries and MLflow capabilities, provides a complete, real-time workflow of data-to-insights-to-action.
To set up a meeting at the Spark + AI Summit, contact email@example.com.