Machine Learning (ML) Framework

A Machine Learning (ML) Framework is a AI framework for machine learning systems.

• Context:
  • It can (typically) contain ML Libraries and ML Tools.
  • ...
  • 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.
  • ...
  • It can be a ML Pipeline Framework, ...
  • It can be compatible with an ML Platform.
  • …
• 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).
  • …
• Counter-Example(s):
  • Machine Learning Tool.
  • Machine Learning Model Deployment System,
  • Machine Learning as a Service (MLaaS).
• See: Data Processing Framework, Machine Learning System, Deep Learning.

---

---

---