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Any AI Stack, Any AI Algorithm, Anywhere


Manage catalog of AI Models, datasets, documentation and results

Manage model versioning, dependencies, datasets and snapshots

Compare model performance and optimize hyperparameters

200+ reusable assets (connectors, data engineering components, algorithms)

25+ AI/ML templates for typical use cases

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Demo Image

Unified AI Orchestrator


Trigger model execution runs on any runtimes like Tensorflow, SparkML, H2O, MxNet, Theano, PyTorch, AWS / Azure from a console

Publish your validated model to the repository and reuse them as APIs

Avoid vendor lock-in and insulate user experience from technical nuances

Simplified User Experience


User is agnostic of nuances in cloud platforms and AI/ML tools

User Experience is the same irrespective of any new technological advancements because of the meta model driven platform

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Demo Image

Intelligent Assistant for AI


Model recommendation for specified model objectives

Automated feature selection based on dataset and model objective

Intuitive insights on model metrics and performance

Integrated Data Preparation & AI Modelling Environment


AiLens has a unified graphical interface for building Data Engineering and AI/ML pipelines

Intuitive job submission and monitoring framework, without intervention from Admins

Code Authoring environment for advanced users

Data Scientists have note book support for R, Python, Spark ML and TensorFlow

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Demo Image

Seamless Enterprise Security Integration


Integrate with enterprise security systems including LDAP, AD, Kerberos, Sentry, AWS/Azure IAM

Secured integration with external entities (academia, AI service providers) to consume AI models, and datasets

Inbuilt encryption and role based access control support for infrastructure, data and modelling layers



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