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  • Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile
  • Create, train and optimize deep learning models for various data science problems on big data
  • Learn how to leverage BigQuery to explore big datasets
  • Use Google’s pre-trained TensorFlow models for NLP, image, video and much more
  • Create models and architectures for Time series, Reinforcement Learning, and generative models
  • Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications

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.

  • Get well versed in GCP pre-existing services to build your own smart models
  • A comprehensive guide covering aspects from data processing, analyzing to building and training ML models
  • A practical approach to produce your trained ML models and port them to your mobile for easy access
Page Count 500
Course Length 15 hours 0 minutes
ISBN 9781788393485
Date Of Publication 29 Apr 2018


Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 15 years' professional 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

V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams.

Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes.

Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.

Alexis Perrier

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.