Jack Ploshnick

Customer Data Scientist
Splice Machine

Jack Ploshnick is a Customer Data Scientist at Splice Machine. His work focuses on using analytics to support the sales and marketing teams, as well as onboarding new customers. Prior to Splice Machine, Jack worked in politics as a data scientist. Jack received his undergraduate degree from Washington University in St. Louis.

Unified MLOps: Feature Stores & Model Deployment
Wednesday, January 20 | 
11:40 AM - 
12:10 PM

If you’ve brought two or more ML models into production, you know the struggle that comes from the complex process. This talk will teach you 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 an open-source platform that allows for deployment of machine learning models as intelligent tables inside of our unique hybrid (HTAP) database. When new data is inserted into these tables predictions are automatically generated by, and stored in, the same database table. This integrated structure takes away the need for endpoints and containers, meaning models can be deployed with just one line of code.

The same HTAP database that makes database model deployment possible powers a world class feature store as well. Most feature stores simply tape together an analytical SQL engine and an operational key-value store, resulting in latency and unnecessary data duplication. Splice Machine’s unified SQL architecture allows for better performance, and is easier to use.

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 start Splice Machine, and how it solves the MLOps problems many in the industry face today.

Hands-On Feature Store and Model Deployment with Splice Machine
Thursday, January 28 | 
09:00 AM - 
12:00 PM

Have you tried to put multiple ML models into production? Managing data sources, feature engineering pipelines, model development, and deployment endpoints is hard enough for one model, but it gets exponentially harder when you have multiple.

In this hands-on demonstration you will learn to use Splice Machine’s platform to build a feature store, train your favorite ML models on data from the feature store, and deploy your model as an intelligent table inside of our database.

We will go from raw data to deployed models in production with no duplicated engineering pipelines, no duplicate data, and no endpoints — but with full security, governance, and transparency.

The workshop will conclude with a modeling competition where the person with the most performant model can win some Splice Machine swag!

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