Hands-On Machine Learning on Google Cloud Platform

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

Key Features

• Get well versed in Google Cloud Platform preexisting services to build your own smart models.
• A comprehensive guide covering all key aspects - from data processing, analyzing to building and training machine learning models
• A practical approach to productionize your trained ML models and port them to your mobile for daily access

Book 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. You will get a practical understanding of deep learning models with their architectures to understand their strengths and weaknesses. Every Deep Learning model is implemented with a relevant dataset and problem to be solved.

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 learn

• Experience the power of the Google Cloud Platform to build data-based applications for dashboards, web, and mobile
• Create, train and optimize Deep Learning models for all types of 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, Sound, Video & much more
• Go beyond Google's Machine Learning APIs and create models and architectures for Time series, Reinforcement Learning, and generative models
• Practice creating, evaluating and optimizing Tensorflow and Keras models for a wide range of applications

Who 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

Alexis Perrier is a data science consultant with a background in signal processing and stochastic algorithms.

A former Parisian, Alexis is now actively involved in the D.C. data science community as an instructor, blogger, and presenter. Alexis is also an avid jazz and classical music fan, a book lover and proud owner of a real chalk blackboard on which he regularly tries to share his fascination with mathematical equations with his 3 children.

He holds a Master in Mathematics from Université Pierre et Marie Curie Paris VI, a Ph.D. in Signal Processing from Telecom ParisTech and currently resides in Washington D.C.

Follow him on twitter @alexip

Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research was focused on machine learning applications in the study of the urban sound environments. He works at Built Environment Control Laboratory - Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years of work 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 of 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 start-up. He is an MBA graduate from IIM Calcutta and also an electronics and communications engineer.

He worked in the fields of credit risk analytics, supply chain analytics, and consulting for multiple FMCG companies to identify ways to improve their profitability.

His interests lie in translating a business problem into a data-related problem by demystifying complexity in data science and identifying ways to further embed analytics in business.