MLOps & Infrastructure · beginner · concept 136 of 187
ML Frameworks (PyTorch, TensorFlow, JAX)
A framework supplies three things a hand-written model would need: automatic differentiation, meaning gradients computed from a recorded graph of operations rather than derived by hand; tensor ops dispatched to vendor GPU kernels; and primitives for splitting a model across devices. PyTorch carries most research code, JAX is the transformation-based option behind much of Google DeepMind's work, TensorFlow persists in older production and mobile stacks. The common error is treating the choice as a performance decision. Speed comes from kernels, compilers and memory layout, and portability off NVIDIA hardware is where frameworks actually differ.
Key terms
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Where you meet it in the real world
Training and serving nearly every production model: LLM pretraining runs, vision models in autonomous driving stacks, recommendation systems, on-device speech and camera features via mobile runtimes
Videos
Andrej Karpathy · YouTube
freeCodeCamp.org · YouTube
Guides and articles
PyTorch
Google / TensorFlow
Courses, papers, and more