Machine Learning (ML) Framework
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A Machine Learning (ML) Framework is a AI framework for machine learning systems.
- Context:
- It can (typically) contain ML Libraries and ML Tools.
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- It can range from being a Neural Networking Framework, Decision Tree Ensemble Framework, ...
- It can range from being a Distributed ML Framework, ...
- It can range from being an Open Source ML Framework to being a Proprietary ML Framework.
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- It can be a ML Pipeline Framework, ...
- It can be compatible with an ML Platform.
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- Example(s):
- Machine Learning Framework Categories:
- Traditional Machine Learning Frameworks: Designed for algorithms such as regression, classification, and clustering (e.g., scikit-learn).
- Distributed Machine Learning Frameworks: Provide scalability for large datasets and parallel processing (e.g., Spark MLlib Module, ML Pipeline Framework).
- Deep Learning Frameworks: Specialized for neural networks and large-scale AI applications (e.g., TensorFlow, PyTorch, Chainer, Apache MXNet).
- Proprietary Frameworks: Enterprise-focused platforms with proprietary features (e.g., Microsoft Cognitive Toolkit).
- High-Level API Frameworks: Simplify model development with user-friendly interfaces (e.g., Keras, Gluon).
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- Machine Learning Framework Categories:
- Counter-Example(s):
- See: Data Processing Framework, Machine Learning System, Deep Learning.