Actionable Model Catalog

At Datatron, we make the life of Data Scientists easy. With our reliable Model Catalog, your models will be versioned and easily found.

ai model library

Datatron bridges the gap between Data Scientists and DevOps. Once the models land on the catalog,
we will make sure DevOps have all the information they need.

Model Import within Seconds

  • Powerful support for any model from any framework
  • Simple yet reliable connections to your model registry
  • Intuitive interface that bridges teams seamlessly

Model Management with Ease

  • Powerful model versioning system available by default
  • Transparent model visualization to customized teams
  • Flexible features to easily manage models in large scale

Model Availability within Our Promise

  • 24/7 services with no down time
  • Reliable cataloging that keep your business assets safe
  • Versatile support to connect models beyond servers and entities

Model Catalog Overview

Feature: Centralized place for model management

Benefit: The only place Data Scientists need to manage. DevOps shall have enough information from the catalog to proceed with deployment

Model Registry & Versioning

Feature: Version control

Benefit: Always know which models have been used in the past, and always able to reuse any past model

Model Repository Connection:

Feature: Connector to customer’s model registry

Benefit: Simple and safe connection to existing model registry

Quick Deployment Pipelines:

Feature: Fast-track from model upload to deployment

Benefit: Guided, single-stop deployment from end to end for MLOps/Model Ops teams

Model Search & Tagging

Feature: Mechanics for model searching and categorizing

Benefit: Easy to find any model or any group of models

whitepaper

5 Reasons Your AI/ML Models are Stuck in the Lab

AI/ML Executive need more ROI from AI/ML? Data Scientist want to get more models into production? ML DevOps Engineer/IT want an easier way to manage multiple models. Learn how enterprises with mature AI/ML programs overcome obstacles to operationalize more models with greater ease and less manpower.

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whitepaper

Life Cycle of Machine Learning Models

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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whitepaper

Unique Challenges Of Machine Learning Models In Production

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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whitepaper

Model Deployment

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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whitepaper

Model Monitoring

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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whitepaper

Model Governance & Management

Production-grade machine-learning models require strong deployment framework in order to reduce the time it takes to iterate a model faster, deploy new features quickly, and train on incoming data faster.

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