This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases. Understand best practices in TensorFlow model patterns and ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating prebuilt models in TensorFlow Hub Make informed design choices about data ingestion, training paradigms, model saving, and inferencing Address common scenarios such as model design style, data ingestion workflow, model training, and tuning

Use TensorFlow Enterprise with other GCP services to improve the speed and efficiency of machine learning pipelines for reliable and stable enterprise-level deployment Key Features Build scalable, seamless, and enterprise-ready cloud-based machine learning applications using TensorFlow Enterprise Discover how to accelerate the machine learning development life cycle using enterprise-grade services Manage Google's cloud services to scale and optimize AI models in production Book DescriptionTensorFlow as a machine learning (ML) library has matured into a production-ready ecosystem. This beginner's book uses practical examples to enable you to build and deploy TensorFlow models using optimal settings that ensure long-term support without having to worry about library deprecation or being left behind when it comes to bug fixes or workarounds. The book begins by showing you how to refine your TensorFlow project and set it up for enterprise-level deployment. You'll then learn how to choose a future-proof version of TensorFlow. As you advance, you'll find out how to build and deploy models in a robust and stable environment by following recommended practices made available in TensorFlow Enterprise. This book also teaches you how to manage your services better and enhance the performance and reliability of your artificial intelligence (AI) applications. You'll discover how to use various enterprise-ready services to accelerate your ML and AI workflows on Google Cloud Platform (GCP). Finally, you'll scale your ML models and handle heavy workloads across CPUs, GPUs, and Cloud TPUs. By the end of this TensorFlow book, you'll have learned the patterns needed for TensorFlow Enterprise model development, data pipelines, training, and deployment. What you will learn Discover how to set up a GCP TensorFlow Enterprise cloud instance and environment Handle and format raw data that can be consumed by the TensorFlow model training process Develop ML models and leverage prebuilt models using the TensorFlow Enterprise API Use distributed training strategies and implement hyperparameter tuning to scale and improve your model training experiments Scale the training process by using GPU and TPU clusters Adopt the latest model optimization techniques and deployment methodologies to improve model efficiency Who this book is forThis book is for data scientists, machine learning developers or engineers, and cloud practitioners who want to learn and implement various services and features offered by TensorFlow Enterprise from scratch. Basic knowledge of the machine learning development process will be useful.