Publisher's Synopsis
Are you ready to conquer the complexities of TensorFlow and deliver real‐world machine learning systems that scale? Many developers struggle to move beyond proof-of-concepts-models that work on a laptop but buckle under production demands.
TensorFlow Machine Learning Tutorial for Developers provides the step-by-step playbook you need to build, optimize, and deploy production-ready models with confidence. From crafting high-throughput data pipelines and writing custom training loops with tf.GradientTape to applying mixed-precision, pruning, and quantization, this book transforms theory into practice. You'll master end-to-end workflows-containerized training on GPUs, serving with TensorFlow Serving or FastAPI, autoscaling in Kubernetes, serverless inference on Cloud Functions, and continuous retraining with TFX.
What you'll achieve:
Efficient Data Engineering: Implement tf.data pipelines that load, augment, and shard data for multi-GPU/TPU training.
Advanced Model Development: Build models using tf.keras Sequential and Functional APIs, custom layers, attention blocks, and transfer learning modules.
Performance Tuning: Apply mixed-precision training, XLA compilation, and distribution strategies to accelerate throughput.
Edge and Cloud Deployment: Package models in Docker, deploy on Kubernetes with autoscaling, or host serverless TFLite microservices on AWS Lambda and Cloud Run.
Robust MLOps Practices: Set up monitoring with Prometheus, Grafana, TensorBoard, detect data drift with TFDV, and automate CI/CD via GitHub Actions.
Are you curious how to scale a Transformer-based chatbot, run inference on a microcontroller, or automate a TFX pipeline that retrains itself? This hands-on guide delivers complete, ready-to-run code examples-no fluff, no theory overload.
Take the next step to transform your machine learning skills into production excellence. Grab your copy of TensorFlow Machine Learning Tutorial for Developers today and start building models that perform, scale, and endure.
