Accelerate ROI and lower the total cost of model development.

Unify & manage all your data science in one place.

Model Building

Pre-packaged, self-service sandbox environments.

Model Training

Scalable training environments with secure access to big data.

Model Deployment

Flexible, scalable, endpoint deployment

Model Monitoring

End-to-end visibility across the ML pipeline

Cloud hosted workspaces

Provision containerized environments on-demand with just a few mouse clicks—whether they’re transient for development and testing, or long-running for a production workload.

Create New Tasks in any language

Collaborate with code across teams

Use any frameworks

Connect data from any source

Make disparate data assets accessible and available for Model Development. Connect data from any cloud, on-premises, or proprietary system using collection of 300+ Connectors

Scheduled Updates

Automate replications with recurring incremental updates.

Extensible with your own connectors

Build connectors in the language of your choice

Connect data from any source through katonic model Development

Transform your data into features to train models and make predictions

Transform raw data into feature values, store the values, and serve them for model training and online predictions using Katonic’s Integrated Feature Store.

Ensure consistency across training and serving

Guarantees you’re serving the same data to models during training and inference, eliminating training-serving skew.

Automate feature transformations

Orchestrate data pipelines to generate backfills and continuously compute fresh feature values.

Transform your data into features to train models and make
predictions

Quality models built fast at scale

Build high-quality models and release them to production faster, with self-serve access to the latest tools and scalable compute.

Katonic supports all of these popular platforms and frameworks plus many, many more.

Deploy Models to Production in Seconds

Deploys the model’s native runtime image (e.g. Python, R, H2O, etc) into a containerized endpoint in just a few click with elastic scaling, high availability, and reliability.

Integration with the model registry allows data scientists to track model versions and seamlessly update models when needed.

Generate predictions for both Batch and real time processing.

Configure canary deployment for incremental model rollouts.

>Deploy Models to Production in Seconds using Katonic MLOps Platform

One-click App Deployment

Deploy apps at a click of a button straight from your Git to go quickly from data to the app, from prototype to production. Share apps in one click and collaborate instantly with live code updates.

Katonic supports the following app frameworks :

Continuous Model monitoring

Monitor the effectiveness and efficiency of a deployed model with a simple dashboard integrated with Model Registry and Feature Store.

Get real-time insights and alerts on model performance and data characteristics.

Debug anomalies and initiate trigger to execute ML production pipelines to retrain the models with new data, depending on your use case

Continuous Model monitoring of ML Production

Security and Control

Katonic MLOps Platform provides multi-tenancy and data isolation to ensure logical separation between each project, group, or department within the organization. The platform integrates with enterprise security and authentication mechanisms such as LDAP, Active Directory. Different project teams, groups, or departments across the enterprise can share the same infrastructure and access the same data sources for their AI / ML and Big Data analytics workloads

Seamlessly integrates with your identity infrastructure using existing LDAP or SSO.

Katonic is ISO 27001 certified, and a copy of our report is available upon request.