Data & ML Frameworks
A list of frameworks and libraries specialized for Data & ML Frameworks development.
| No | Name | Description | Trend | License | Language | Official Site |
|---|---|---|---|---|---|---|
| 1 | TensorFlow | Open-source machine learning framework developed by Google. Excellent scalability in large-scale production environments, supporting enterprise-level AI development. | Industry standard for machine learning deployment in production environments. Continued enterprise adoption growth through TensorFlow Serving and cloud integration. | Apache 2.0 | Python C++ | Official |
| 2 | PyTorch | Machine learning framework developed by Facebook featuring dynamic computation graphs. Popular in academia and research institutions for flexibility and intuitive Pythonic API suitable for R&D. | Established as top choice in research and experimentation fields. Rapid increase in academic paper usage due to adoption by major tech companies and universities. | BSD-3-Clause | Python | Official |
| 3 | scikit-learn | Representative machine learning library for Python. Provides rich classical machine learning algorithms, widely used from beginners to experts. Standard tool for data science. | Standard choice for machine learning introduction and small to medium-scale projects. Continued use as essential library for education and practical prototyping. | BSD-3-Clause | Python | Official |
| 4 | Apache Spark | Distributed computing framework for large-scale data processing. Enables scalable machine learning through MLlib library. Standard platform for big data analytics. | Continued demand due to big data and cloud computing proliferation. Important for enterprise data pipeline construction with both batch and real-time support. | Apache 2.0 | Scala Python Java | Official |
| 5 | Keras | Neural network library functioning as high-level API for TensorFlow, PyTorch, and JAX. Enables rapid prototyping of deep learning models with simple API. | Established as standard choice for deep learning beginners. Continues to play important role in education and prototyping fields. | Apache 2.0 | Python | Official |
| 6 | Pandas | Python data manipulation and analysis library. Essential tool for processing, transforming, and analyzing structured data. Significantly improves data scientist work efficiency. | Unshakable position as foundational library in data science field. Essential for wide range of data work from ML preprocessing to business analysis. | BSD-3-Clause | Python | Official |
| 7 | NumPy | Foundational library for Python scientific computing. Provides multi-dimensional array operations and numerical computation features, functioning as basis for almost all Python data science libraries. | Absolute position as core of Python data science ecosystem. Increasing importance with growth of machine learning and AI fields. | BSD-3-Clause | Python C | Official |
| 8 | Apache Airflow | Programmable workflow management platform. Integrated management of data pipeline construction, scheduling, and monitoring. Important in MLOps and data engineering. | Rapidly increasing demand due to data engineering and MLOps proliferation. Expanding enterprise adoption as standard tool for complex data pipeline management. | Apache 2.0 | Python | Official |