Ship the trained model. Save in the Keras or SavedModel format, reload for inference, and export to TensorFlow Serving or TFLite — the TF equivalents of TorchScript/ONNX.
Why: unlike PyTorch (where you save the state_dict and must rebuild the architecture to load it), Keras can save the ENTIRE model — architecture, weights, and optimizer state — in one file and reload it ready to use. When: save after training; load in a serving script with no need to redefine the model class. Where: the .keras format is the modern default; SavedModel is the format for TF Serving and cross-language deployment.
# Save the whole model (architecture + weights + optimizer) — no rebuild needed:
model.save("model.keras")
from tensorflow import keras
loaded = keras.models.load_model("model.keras") # ready to predict immediately
loaded.predict(X_test[:5])
# For deployment infra, export the SavedModel directory format:
model.export("saved_model/") # -> TF Serving / TFLite / other languagesWhy: TensorFlow’s deployment story is its historic strength — the same model exports to TensorFlow Serving (production REST/gRPC serving), TFLite (mobile/edge), and TensorFlow.js (browser) — the TF analogs of PyTorch’s TorchScript/ONNX/mobile paths. When: pick the target for your environment: Serving for backends, TFLite for on-device, TF.js for the browser. Where: this breadth of first-class deployment targets is a common reason teams choose TensorFlow over PyTorch.
From one SavedModel, deploy anywhere:
TensorFlow Serving production model server (REST + gRPC), versioning
TFLite mobile / embedded / edge (quantized, small)
TensorFlow.js run in the browser / Node.js
PyTorch analogs: TorchScript/ONNX (serving), ExecuTorch/Mobile, ONNX.js.
TF's mature, varied deployment targets are a key reason teams pick it.Why: now that you know both, the choice is about ecosystem and team, not capability — they train the same models to the same accuracy. When: PyTorch leads in research and has the most flexible, Pythonic feel; TensorFlow/Keras leads in production tooling (Serving, TFLite, TF.js) and offers the fastest path from idea to trained model via fit(). Where: many teams prototype in PyTorch and some deploy via TF tooling — and Keras 3 now even runs on a PyTorch or JAX backend, blurring the line.
Both train the same models to the same results. Choose on ecosystem:
PyTorch research default, most flexible, explicit control,
huge model ecosystem (Hugging Face)
TensorFlow/Keras fastest to a trained model (fit()), best-in-class
production deployment (Serving, TFLite, TF.js)
Not a rivalry to win — a tool to match to the job. (Keras 3 even runs on
a PyTorch or JAX backend, so the frameworks are converging.)