M2D2
M2D2 is an open source no-code/low-code platform for building machine learning models. It allows users to train, evaluate, and deploy ML models visually without coding.
nocode
lowcode
ml-models
visual-interface
M2D2: Open Source No-Code/Low-Code Machine Learning Platform
Build machine learning models with ease using M2D2, a visual platform for training, evaluating, and deploying ML models without coding.
What is M2D2?
M2D2 is an open-source no-code/low-code machine learning platform that allows users to build, train, evaluate and deploy machine learning models visually without writing any code. It provides a drag-and-drop interface to construct ML pipelines, leveraging popular Python libraries like Pandas, Scikit-Learn, PyTorch, and TensorFlow under the hood.
Some key capabilities and benefits of M2D2 include:
- Intuitive visual interface to assemble ML pipelines
- Support for various ML algorithms including regression, classification, clustering, NLP, etc.
- Real-time visualization and metrics for model performance
- One-click deployment of models via REST APIs
- Version control and reproducibility of ML experiments
- Collaboration features for teams to work together
- Output clean, production-ready code for models
- Extensibility to incorporate new data sources, models, and deployments
M2D2 simplifies machine learning, allowing domain experts and citizen data scientists to leverage ML without coding expertise. Its no-code simplicity combined with transparency into model behavior makes it well-suited for responsible AI applications.
M2D2 Features
Features
- Visual interface for building ML models
- Support for common ML algorithms like regression, classification, clustering
- Model training, evaluation and deployment
- Integration with data sources
- Collaboration features
Pricing
- Open Source
- Free
Pros
No coding required
Intuitive drag-and-drop interface
Quickly build and iterate on ML models
Accessible to non-technical users
Open source and free
Cons
Limited customization compared to coding ML
Less flexibility than general-purpose ML platforms
Limited model evaluation tools
Not suitable for complex ML systems
Official Links
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