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Key benefits

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

Description

TensorFlow 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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Kay T Mar 08, 2021

This book is unlike myriads of Tensorflow or machine learning books in the market. If you are interested in enterprise level use and deployment of Tensorflow models, this is the right book. This book helps me to go beyond the baby-steps of learning how to build Tensorflow ML models. All examples throughout this book uses either datasets or TFRecord data structure. I did not have a good understanding about these data structure before I bought and read this book. I often wonder why we need such data structures. After I read this book, I now understand that in an enterprise or production level, knowing how to handle distributed data is a must-have skill. I am glad that the author chose to use such enterprise-relevant data structures throughout the examples in this book. I would say this is a unique aspect of this book which differentiates it from other Tensorflow books. Another nice touch is that instead of teaching you how to build a ML model, it uses pre-built models Tensorflow Hub, and show me how to make it work for my own data. I learned transfer learning for the first time with book.To make most use of this book, it is important to clone the accompanying GitHub directory.For Tensorflow to be useful at enterprise level, it is important to have cloud integration. This book also helped me get started with learning how to use Google Cloud AI Platform with the integration to BigQuery data warehouse. Further, this book also contains step by step instructions on how to leverage cloud TPU and GPU to perform distributed training job. As a matter of fact, I now realize how important it is to use cloud TPU or GPU for time consuming job such as hyperparameter optimization. And the book shows me exactly how to do it. This book also did a very good job helping me learn how to different hyperparameter tuning methods work. I learned how hyperband algorithm works for the first time. That’s a delightful surprise.This book also describes how model optimization works, and why it is important. I didn’t realize that model size can be reduced by so much and yet retains similar or identical accuracy. Now I realize that once the model is built, optimization is always a good idea to make it more light-weight. And finally, when it comes to deployment, this book helped me understand how to serve the model behind a REST API using Tensorflow Serving. Model serving is a complicated issue in enterprise setting. This book helps me acquire the table stake knowledge about serving a model using Docker container. With the way described by this book, it turned out to be much easier than I thought.So overall, I would rate this book as a five-star book, and really appreciate the thoughts and works put in this book by the author. I definitely recommend it for anyone who has Tensorflow experience, and looking to take their skills to another level, which is much more relevant and practical for enterprise use. Well done.

Amazon Verified review

Christian P. Mar 09, 2021

Cloud computing is a relevant tool for companies to achieve their digital transformation, especially for those developing data-driven business models based on Deep Learning Frameworks as a core technology. Commence in cloud technologies can be overwhelming for beginners due to the tons of online tutorials, material, and documentation that are not always updated, creating frustration and delaying these technologies' adoption.The book is a useful guide and a great starting point for deploying the first AI-based applications for those who initiate Cloud Computing. Also, it is an excellent complementary material for those currently working as MLOps Engineers that want to understand advanced options in TensorFlow Enterprise and Google Cloud Computing. It includes sections with practical hands-on material easy to read and follow. Chapters handle relevant topics such as creating a data warehouse on the cloud, accessing the data efficiently using Tensorflow from different pythonic formats such as Pandas DataFrames or Numpy arrays, and small but valuable tips about using TPU instead of using a GPU. The hand-on material is available on Github, and such is a good source to start developing your ideas

Amazon Verified review

laksh Apr 30, 2021

One single source of reference material for tensorflow. Practical guide.

Amazon Verified review

Aishwary Feb 23, 2021

This book is a perfect introduction to TensorFlow, from learning basics to advanced features like model deployment in production. The best part about this book is sample code as a part of the explanation and images to illustrate the UI components on Google Cloud Platform. This book has an excellent use case to work google cloud AI notebooks while leveraging big data suite tools (tools like BigQuery). One of the other good things about this book is that it does not leave any concepts half explained. I liked the section on transfer learning and hyperparameter tuning (section 3 - chapter 4 and 6) the most. It also has details about working with TFRecords, which is an essential feature to work with data in the real world. Even if you do not work to deploy models in production, I would recommend every deep learning practitioner to read this book to get a perfect experience with all the concepts that one requires to leverage on a day-to-day basis.

Amazon Verified review

SID ALLA Dec 14, 2020

I have been doing deep learning modeling using tensorflow programming for couple of years and its hard to actually build the model picking right number of GPU machines , setting them up properly, connecting data sources, buidl train models and then take the model to production. I have used the other public clouds but its much much easier on google cloud as they created Tensorflow in the first place. I also bought some beefy gaming machines with Nvidia chipsets but always had the ceremonial steps to do before i could do anything practical at cloud scale.This books explains clearly how to set up the sources in data warehouse, how to create notebooks, build models and deploy them without breaking the bank as its all taken care by Google Cloud.I have to point out the toughest parts of Deep Learning are the scaling using TPUs and GPUs and this book covers those aspects too. Not to underestimate, serving models are tricky and there are just too many options to do it. This book walks you through a good way to serve such models.Frankly this book saves time you will spend going through the many docs online and lets you quickly start from introduction and takes you to production.

Amazon Verified review

Learn TensorFlow Enterprise: Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

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