Modern ML & AI Ecosystem
Open-source machine learning and deep learning frameworks optimized for enterprise workloads and research.
Supported Frameworks
TensorFlow
End-to-end open-source ML platform with flexible architecture and enterprise capabilities.
PyTorch
Dynamic computation with Python-friendly APIs for research, data science, and production deployment.
ONNX
Open format for interchangeable ML model interoperability and cross-framework deployment.

Scikit-learn
Classic machine learning framework for traditional ML methods and data preprocessing.
Feature Highlights
Enterprise ready ML tooling with seamless integration of model training, optimization, and deployment workflows.
Distributed Training
Optimized for multi-GPU/TPU clusters across on-prem and cloud platforms.
Cloud-Ready Workflows
Pre-configured cloud deployment templates for AWS/GCP and Azure ML.
AutoML
End-to-end autoML integration across hyperparameter search and model selection.
Explainability
Built-in model explainers for AI transparency and regulatory compliance.
Seamless cloud integrations with GCP, AWS, and Azure ML.
ML and AI Workloads
Deploy powerful machine learning and deep learning models with enterprise-grade MLOps workflows.
AI Training
- v1.5
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TensorFlow & PyTorch
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ML Frameworks
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FrameworksCommunity
Community Driven
Over 5000+ contributors
GitHub / GitHub Stars 4.9k ★
Open Source Code
MIT License
Machine Learning frameworks.
MIT License
GitHub repo for the frameworks.
- TensorFlow ML
- PyTorch
- PyTorch Lightning