CONCEPTS & TURNING POINTS / 1 MIN READ
Machine-Learning Frameworks
On this page
Frameworks are the software layers that let researchers define, train, and deploy models. This sub-article expands Section 2.2 of Hardware & Software.
Deep-Learning Frameworks
- TensorFlow (Google, 2015) — production-oriented, with Keras integration.
- PyTorch (Meta, 2016) — Pythonic, dynamic graphs; dominant in research.
- JAX (Google) — functional, high-performance research code with
jit,vmap, andgrad.
Classical ML and Statistics
- scikit-learn, XGBoost, and statsmodels cover classical models and pipelines.
- Hugging Face Transformers provides a unified interface to pretrained models.
Automatic Differentiation
- Frameworks compute gradients automatically, enabling backpropagation.
- Mixed-precision and distributed-training utilities are built in.
Choosing a Framework
- Research favors PyTorch/JAX; production often uses TensorFlow or ONNX runtime.
- Interoperability via ONNX lets models move between runtimes.
Why It Matters
Frameworks lower the barrier to building models, standardize workflows, and connect hardware kernels to high-level code.
Sources
- Paszke, A. et al. (2019). PyTorch: An Imperative Style.
- Abadi, M. et al. (2016). TensorFlow: A System for Large-Scale Machine Learning.
Back to Hardware & Software.