Splice Machine to Present on ML and Operational AI at Spark + AI Summit 2019

“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.”

Session Summaries
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 MLflow
Gene 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.

For more information, visit www.splicemachine.com. To set up a meeting at the Spark + AI Summit, contact [email protected].

About Splice Machine

Splice Machine is the operational AI data platform to simplify digital transformation. Unlike other data platforms that require duct taping separate systems together, the Splice Machine data platform is a scale-out SQL RDBMS, data warehouse and machine learning management solution in one. The Splice Machine platform powers intelligent, mission-critical applications that are woven into the operational fabric of companies in the financial services, healthcare, industrial and consumer verticals to improve operational efficiency, eliminate unnecessary costs and deliver superior service. The Splice Machine data platform can be deployed on-premise or as a fully-managed cloud service.

Source: Splice Machine