ModelFoundry
ModelFoundry is an open-source platform for developing, training, and deploying AI models. It provides tools to build, visualize, version, and monitor models with integrated lifecycle management.
opensource
ai-models
model-training
model-deployment
model-monitoring
ModelFoundry: Open-Source AI Platform
Develop, train, deploy & manage AI models with a comprehensive platform featuring tool integration for seamless model lifecycle management
What is ModelFoundry?
ModelFoundry is an open-source machine learning operations (MLOps) platform for the full lifecycle management of AI models. It provides a unified interface to develop, train, deploy, and monitor models across various frameworks like PyTorch, TensorFlow, and scikit-learn.
Key capabilities and benefits of ModelFoundry include:
- Model Registry - Store, version, and track all model artifacts like code, datasets, metrics etc.
- Automated Model Building - Build and recreate models with custom Docker containers.
- Collaboration Tools - Manage access and permissions for teams to collaborate.
- Model Deployment - Use pre-made deployment templates to serve models via REST and gRPC endpoints.
- Monitoring & Drift Detection - Monitor production models for data drift to maintain quality.
- Model Insights - Get detailed analytics on model performance and fairness.
- Open Source - Free and available under the Apache 2.0 license.
With its end-to-end platform, ModelFoundry accelerates the development and oversight of impactful, ethical and scalable AI systems for both small teams and enterprises.
ModelFoundry Features
Features
- Open-source platform for developing, training and deploying AI models
- Provides tools to build, visualize, version and monitor models
- Integrated model lifecycle management
- Supports major frameworks like PyTorch, TensorFlow, Keras
- Model registry and model store
- Collaboration tools
- MLOps capabilities like CI/CD pipelines, model monitoring
Pricing
- Open Source
Pros
Open source and free to use
End-to-end model development and deployment capabilities
Visualization and monitoring helps debug models
Collaboration features help teams work together
MLOps features automate model retraining and deployment
Cons
Limited documentation and community support
Not as feature rich as commercial MLOps platforms
Some components still in beta
Lacks some advanced features like feature stores
Official Links
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