Kinetica enables data scientists to explore and prepare all their data at the speed of thought. Improve the accuracy and nuance of discoveries by examining your full data corpus vs. downsampling data. Perform large scale analytical queries and data analysis and visualization of billions of geospatial data points interactively for exploration with server side rendering.
Rapidly train machine learning models with larger data sets to improve accuracy of inferences. Deploy a full lifecycle solution for ML accelerated by GPUs: managed Jupyter notebooks, model training via RAPIDS, and automated model deployment and inferencing in the Kinetica platform.
Increase the accuracy of machine learning data analytics models by utilizing different types of feature pipeline calculations. Perform large-scale feature transformations such as normalizations, filters, joins, and complex geospatial functions directly in the database. Combine SQL, geospatial, graph, time series, and text search analyses and rapidly process them inline as inputs to machine learning models.
Automate model deployment in Kinetica utilizing Kubernetes supporting continuous, on-demand, and batch inferencing modes. No need to worry about deployment, network configuration, pipeline management, or scaling. Once deployed, Kinetica automatically orchestrates the full data pipeline – from ingestion, to feature transformation, to inferencing and back to database and downstream applications.
Track, govern, and audit machine learning data analytics that’s part of your ML workloads. Kinetica tracks the full data lineage, including raw data, feature transformations, and model output. Easy-to-use search tool provides an instant ability to do a full model audit or find a “needle in a haystack” for a specific inference.