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 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
Who this book is for
This 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.
