Splice Machine, provider of the real-time AI platform built on the only scale-out SQL database with built-in machine learning, today announced version 3.1 of its database, which introduces new features and functionality to support enterprises with real-time AI projects. With 3.1, Splice Machine has added Spark 3.0 support for its database engine, which adds performance improvements, resource elasticity support on Kubernetes, GPU support, expansions to Spark’s ML libraries, and more.
Splice Machine 3.1 greatly increases transparency of data used to create ML models. A new feature of 3.1 enables developers to query the database back in time with AS OF syntax to a specific date, providing a full audit and lineage for a regulator checking for bias or data drift.
Last November, Splice Machine launched Livewire, an operational AI platform for industrial companies to deploy predictive applications and keep them up and running. With 3.1, Splice Machine has added new native Spark structured streaming ingestion, a new feature that makes streaming data resources incredibly easy to ingest. This is especially valuable for industrial accounts connected to distributed control systems (DCs) and historians, where it is essential to ingest data in real-time as it becomes available.
“We are excited to be powering data engineers and data scientists with the tools they need to break down the chasms that stop ML and AI projects from being successful,” said Monte Zweben, co-founder and CEO, Splice Machine. “With 3.1, we have made vital leaps in database capabilities to successfully operationalize real-time AI applications and bring ML models into production.”
With 3.1, Splice Machine has significantly enhanced its database capabilities, including new foreign key processing, richer trigger support and improved handling, indexes on expressions, and improved import and export capabilities as well as DB2 compatibility. Splice Machine now makes it virtually turnkey to migrate entire applications powered by IBM DB2 to a modern scale-out architecture with machine learning.