Operational AI is Hard, But Splice Machine is Making it Easy

The Silicon Review

Putting machine learning models into production is too hard. Disconnect between Data Scientists and Data Engineers, managing multiple languages and frameworks, and administering container endpoints means that models take months to go into production, if at all. Splice Machine can help with Database Model Deployment, which is the fastest and easiest way to deploy machine learning. Using simple SQL, you can deploy your model as an intelligent table inside of our database. When data is inserted, a prediction is automatically generated and stored in that same database table.

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.

The company is headquartered in the South of Market (SOMA) neighborhood of San Francisco and operates with a global, distributed workforce. The company has a veteran team that has been successful at every level of the software landscape. From successful startups to major software firms, its team has built, managed and supported solutions that have transformed the way businesses operate.

As the provider of the only scale-out SQL RDBMS with built-in machine learning, Splice Machine has driven advancements that others did not think possible. Unlike other feature stores, the Splice Machine Feature Store is built on a single database. This delivers simplicity, scalability, and speed, both in implementation and operation. By choosing the Splice Machine Feature Store over a single cloud option, companies can avoid cloud vendor lock-in – and retain the ability for on-premise hosting.

"We had this sort of a feature store at Airbnb, but it was limited by the fact that we were largely on HDFS. It enabled users to share features, but it didn't solve the online/offline problem. But the solution can obviously be much more elegant if you start with a more amenable database that can function in realtime. Splice Machine seems to be doing exactly that – MLflow integration, database re-injection, Spark lazy loading, easy deployment, and API-less access," said Robert Yi, CDO at Dataframe and former Airbnb data scientist

Remove Friction from Data Science Process

Traditional enterprise data infrastructure consisting of separate transactional, analytical and data science platforms does not provide a viable foundation to power mission-critical machine learning (ML) applications. This architecture has latency built-in at multiple levels.

First, the model development and training phase requires data to be continuously extracted from transactional enterprise applications. Once the model is built and trained, it requires expensive transformations and aggregations to operationalize the features before any predictions can be made. This infrastructure is also not agile enough to trigger the right action in real-time especially where data attributes change rapidly and require the model to be continuously trained on updated data.

Splice ML Manager provides end-to-end lifecycle management for the ML models, thereby streamlining and accelerating the design and deployment of intelligent applications using real-time data.

Splice's ML Manager platform, based on MLFlow, has enabled a closed-loop machine learning lifecycle. Its improved API makes it quicker and easier to manage your ML development, from bulk logging of model parameters and metrics to full visibility into pipeline stages and feature transformations. With just a few added lines of code, data engineers can recreate any ML pipeline in seconds. Direct access to the training and testing tables allows data scientists to guarantee new models are evaluated on the same data as currently deployed ones.

Migrate Without Complexity

Too many modern data platforms require you to rewrite lots of legacy code, hire expensive infrastructure engineers, or change your entire stack just to get started. For many companies this is too expensive, too time consuming, and often just not possible. Splice Machine’s full ANSI SQL means you can migrate any legacy database, with little to no rewrites. Don’t want to transfer your data just yet? No problem: With Splice Machine's EXTERNAL TABLE and VIRTUAL TABLE INTERFACE functionality, you can access data directly from any database or storage environment.

The Visionary CEO

Monte Zweben, Chief Executive Officer and Co-founder

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 omnichannel 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.