AiLens (Object Detection)
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
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
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
Model recommendation for specified model objectives
Automated feature selection based on dataset and model objective
Intuitive insights on model metrics and performance
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
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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