TensorFlow: Deployment

Saving & Loading Models

# Save in Keras format (recommended for TF/Keras)
model.save('model.keras')
model = keras.models.load_model('model.keras')

# SavedModel format (for TF Serving, TFLite conversion)
model.export('saved_model/')
# or: tf.saved_model.save(model, 'saved_model/')

model = tf.saved_model.load('saved_model/')

# Save/load weights only (fine-tuning, checkpointing)
model.save_weights('weights.h5')
model.load_weights('weights.h5')

# HDF5 format (legacy)
model.save('model.h5')
model = keras.models.load_model('model.h5')

TF Serving (Production API)

# Serve a SavedModel via REST/gRPC
docker run -p 8501:8501 \
  --mount type=bind,source=/path/to/saved_model,target=/models/my_model \
  -e MODEL_NAME=my_model \
  tensorflow/serving

# REST prediction request
curl -X POST http://localhost:8501/v1/models/my_model:predict \
  -H "Content-Type: application/json" \
  -d '{"instances": [[1.0, 2.0, 3.0, ...]]}'

TFLite — Mobile & Edge

# Convert to TFLite
converter = tf.lite.TFLiteConverter.from_saved_model('saved_model/')
# Optional: quantize for smaller/faster model
converter.optimizations = [tf.lite.Optimize.DEFAULT]  # dynamic range quant
# Full integer quantization (fastest on CPU/microcontrollers):
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

tflite_model = converter.convert()

with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

# Run inference with TFLite interpreter
interpreter = tf.lite.Interpreter(model_path='model.tflite')
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output = interpreter.get_tensor(output_details[0]['index'])

TensorFlow.js

// Run TF models in the browser or Node.js
import * as tf from '@tensorflow/tfjs'
import '@tensorflow/tfjs-backend-webgl'   // GPU in browser

// Load a converted model
const model = await tf.loadLayersModel('/model/model.json')

// Predict
const input = tf.tensor2d([[1, 2, 3, 4]])
const prediction = model.predict(input)
const result = prediction.dataSync()

// Convert from Python SavedModel:
# pip install tensorflowjs
# tensorflowjs_converter --input_format=keras model.keras ./web_model

TensorBoard

# Log metrics, histograms, images during training
tensorboard_cb = keras.callbacks.TensorBoard(
    log_dir='logs/',
    histogram_freq=1,   # log weight histograms every epoch
    write_images=True,  # log model weights as images
)

model.fit(..., callbacks=[tensorboard_cb])

# Launch TensorBoard
# tensorboard --logdir logs/
# Open: http://localhost:6006

• Scalars tab: loss and metrics over epochs

• Histograms: weight distributions — detect vanishing/exploding gradients

• Images: visualize input images or feature maps

• Projector: visualize high-dimensional embeddings with t-SNE/PCA

• Profile: per-step timing — identify bottlenecks in data pipeline or GPU utilization