Unleash Google's Cloud Platform to build, train and optimize machine learning models

Key FeaturesGet well versed in GCP pre-existing services to build your own smart modelsA comprehensive guide covering aspects from data processing, analyzing to building and training ML modelsA practical approach to produce your trained ML models and port them to your mobile for easy accessBook Description

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions.

This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications.

By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.

What you will learnUse Google Cloud Platform to build data-based applications for dashboards, web, and mobileCreate, train and optimize deep learning models for various data science problems on big dataLearn how to leverage BigQuery to explore big datasetsUse Google’s pre-trained TensorFlow models for NLP, image, video and much moreCreate models and architectures for Time series, Reinforcement Learning, and generative modelsCreate, evaluate, and optimize TensorFlow and Keras models for a wide range of applicationsWho this book is for

This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy

Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research is on machine learning applications in the study of urban sound environments. He works at Built Environment Control Laboratory, Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years' experience in programming Python, R, and MATLAB, first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit. V Kishore Ayyadevara has over 9 years' experience of using analytics to solve business problems and setting up analytical work streams through his work at American Express, Amazon, and, more recently, a retail analytics consulting startup. He has an MBA from IIM Calcutta and is also an electronics and communications engineer. He has worked in credit risk analytics, supply chain analytics, and consulting for multiple FMCG companies to identify ways to improve their profitability. Alexis Perrier is a data science consultant with experience in signal processing and stochastic algorithms. He holds a master's in mathematics from Université Pierre et Marie Curie Paris VI and a PhD in signal processing from Télécom ParisTech. He is actively involved in the DC data science community. He is also an avid book lover and proud owner of a real chalk blackboard, where he regularly shares his fascination of mathematical equations with his kids.

Giuseppe Ciaburro

Books