Remove the Friction In Your Data Science Process
Keep your mission-critical intelligent applications continuously learning and improve the quality of their decisions. Improve model performance by running the models in the database and take intelligent actions in real-time.
See Splice Machine’s ML Manager in action.
Benefits for Data Scientists
With Splice Machine, data science teams are able to produce a higher number of more predictive models by experimenting frequently using diverse parameters and algorithms. Leverage updated operational data to concurrently train the model and minimize the time spent wrangling data.
Easily compare a number of models and seamlessly deploy the winner into production. Splice ML Manager provides end-to-end life-cycle management for your machine learning models to streamline and accelerate the design and deployment of intelligent applications using the most updated data.
Key Splice Machine Features
Experiment Freely
With MLflow as part of ML Manager, data scientists have complete freedom to experiment and compare models based on key parameters, versions, and... metrics. Build a feature factory to try out different combinations of features, swap out one feature, add a new one to replace a less predictive one, or use a derived feature into the new run. Complete visibility into the various experiments enables the data scientist to deploy the most predictive model into production and have the experiment artifacts across all runs at their fingertips for model governance purposes.
Train Models Continuously
Splice Manager data platform empowers data scientists to remove the latency in training their models by providing access to the most current data.... With the burden of ETL lifted, data scientists can keep their mission-critical models running at peak performance by continuously training them on the most updated data available.
Deploy Seamlessly
ML Manager provides data scientists with end-to-end model lifecycle management. Once the data scientists have selected their model, MLflow packages models into Docker... images, which can then be deployed into a production environment directly via Sagemaker with one click.
Run Models Faster
The Splice Machine Native Spark DataSource provides dramatic performance improvements for large scale data operations. The Native Spark DataSource works directly on native... DataFrames and RDDs, eliminating the need to serialize data over the wire using the JDBC protocol. Instead of accessing, inserting, or manipulating data one record at a time over a serialized connection, data scientists can now use the DataSource to pull the contents of an entire DataFrame into your database and to push database query results into a DataFrame.
Watch Our ML Manager Demo
Check out our webinar hosted by Splice Machine’s Ben Epstein