Integrate Machine Learning models with your data

Published: 01 May 2019

Integrate your best algorithms and machine learning models seamlessly into your application in a way that will help you deliver real-time experiences, while at the same time giving you the corporate governance and ease of use that you need to scale.

 

Artificial Intelligence

Kinetica seamlessly integrates machine learning models and algorithms with your data, for real-time predictive analytics at scale. By unifying the traditionally siloed workflows of analytics and model execution, Kinetica dramatically simplifies data engineering and active analytical application development.

 

Bring Your Own Algorithm

Bring your models to the data, not your data to the models. Bring existing models and analytics as containers and embed them into your analytical workflows and applications without the heavy lifting of migrating data to and from siloed model execution environments.


Automated Deployment & Data Orchestration

Kinetica automates model deployment on Kubernetes – in continuous, on-demand or batch modes. No need to worry about deployment, network configuration, or scaling. Once deployed, Kinetica automatically orchestrates the full analytical pipeline – from ingest to database to model and back to database and downstream applications.


Pushbutton Distributed Training

Explore data interactively, at scale, across dimensions, to find patterns. Then, experiment rapidly with built-in support for the most popular TensorFlow templates and fully automated distributed training.


Model Audit

Track, govern, and audit data that’s part of your analytics and 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.

 

Build Machine Learning into Active Analytical Applications

Deliver real-time experiences with the governance and ease of use you need to scale

 

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