Every phase before this one ended with a trained model sitting in a Jupyter notebook. Phase 8 is about everything that happens next: feeding that model data fast enough to keep expensive hardware busy, putting it behind an API the rest of your product can call, and — when one GPU isn’t enough — spreading the training itself across many.

You’ll learn

• how the tf.data Data API builds efficient input pipelines: chaining transformations, shuffling, batching, and prefetching so the GPU is never waiting on the CPU
• how to store and stream large datasets using the TFRecord format
• how to drop below tf.keras.fit()tf.keras.fit() to write custom losses, metrics, layers, and a hand-rolled training loop with tf.GradientTapetf.GradientTape
• how to use GPUs for training, and how data parallelism (the mirrored strategy, via tf.distributetf.distribute) and model parallelism let you scale training across multiple devices and machines
• how to automate the search for the best architecture with KerasTuner, and squeeze out extra accuracy by ensembling several models together
• how mixed-precision training (float16 compute, float32 weights) speeds up training on GPU and TPU for close to free
• how to export a trained model as a SavedModel and serve it with TensorFlow Serving over REST or gRPC, with clean version rollouts and rollbacks
• how to shrink a model for phones and embedded devices with TF Lite (quantization, FlatBuffers)
• what deep learning still can’t do, and how local generalization differs from the extreme generalization humans rely on

  flowchart TD
  A["tf.data: load & preprocess"] --> B["Custom training loops (GradientTape)"]
  B --> C["Train at scale (tf.distribute, mixed precision)"]
  C --> T["Tune & ensemble (KerasTuner)"]
  T --> D["Export SavedModel"]
  D --> E["Serve: TF Serving (REST/gRPC) or TF Lite (mobile)"]
  E --> F["Limitations & what's next"]

Phase checklist

1. Loading & Preprocessing Data with tf.data
2. Custom Models and Training Loops (TensorFlow)
3. Distributed Training with tf.distribute
4. Hyperparameter Tuning with KerasTuner
5. Mixed Precision and Multi-GPU Training
6. Serving Models with TensorFlow Serving
7. Deploying to Mobile & Edge with TensorFlow Lite
8. Limitations and the Future of Deep Learning

Prerequisites

• You’ve built and trained at least one tf.keras model (Phases 1–2 of this module).
• Comfortable with NumPy arrays and basic Python file I/O.
• No prior experience with Docker, GPUs, or distributed systems needed — each page starts from the basics.