Hands-On Machine Learning on Google Cloud Platform

Hands-On Machine Learning on Google Cloud Platform
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
Total Pages: 489
Release: 2018-04-30
Genre: Computers
ISBN: 1788398874

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

Hands-On Machine Learning on Google Cloud Platform

Hands-On Machine Learning on Google Cloud Platform
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
Total Pages: 489
Release: 2018-04-30
Genre: Computers
ISBN: 1788398874

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

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author: Aurélien Géron
Publisher: O'Reilly Media
Total Pages: 851
Release: 2019-09-05
Genre: Computers
ISBN: 1492032611

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Building Machine Learning and Deep Learning Models on Google Cloud Platform

Building Machine Learning and Deep Learning Models on Google Cloud Platform
Author: Ekaba Bisong
Publisher: Apress
Total Pages: 703
Release: 2019-09-27
Genre: Computers
ISBN: 1484244702

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Journey to Become a Google Cloud Machine Learning Engineer

Journey to Become a Google Cloud Machine Learning Engineer
Author: Dr. Logan Song
Publisher: Packt Publishing Ltd
Total Pages: 330
Release: 2022-09-20
Genre: Computers
ISBN: 1803239417

Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills Key FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook Description This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is for Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book.

Google Cloud Platform Administration

Google Cloud Platform Administration
Author: Ranjit Singh Thakurratan
Publisher: Packt Publishing Ltd
Total Pages: 224
Release: 2018-09-29
Genre: Computers
ISBN: 1788626605

Make the most of GCP’s offerings to manage your data center workload and optimize deployments Key FeaturesDiscover new techniques to administer, manage, and deploy applications on GCPUnderstand effective solutions for storing, retrieving, and deploying your container imagesExplore various offerings of GCP for operations and security Book Description On-premise data centers are costly to manage. If you need a data center but don’t want to deal with a physical one, Google Cloud Platform (GCP) is the solution. With GCP, you can build, test, and deploy applications on Google’s infrastructure. Google Cloud Platform Administration begins with GCP fundamentals, with the help of which you will deploy your first app and gain an understanding of Google Cloud architecture and services. Furthermore, you will learn how to manage Compute, networking, and storage resources. As you make your way through the book, you will learn how to track and manage GCP’s usage, monitoring, and billing access control. You will also be able to manage your GCP's access and permissions. In the concluding chapters, you will explore a list of different developer tools for managing and interacting with the GCP platform. By the end of this book, you will have learned how to effectively deploy workloads on GCP. What you will learnUnderstand all GCP Compute componentsDeploy and manage multiple GCP storage optionsManage and utilize the networking resources offered by GCPExplore the functionalities and features of the GCP Container Understand the workings of GCP operations such as monitoring and error reporting Discover an immune GCP using its identity and security optionsWho this book is for Google Cloud Platform Administration is for administrators, cloud architects, and engineers who want to leverage the upcoming Google Cloud Platform. Some basic understanding of cloud computing will be useful.

Google Cloud Platform an Architect's Guide

Google Cloud Platform an Architect's Guide
Author: Alasdair Gilchrist
Publisher: Alasdair Gilchrist
Total Pages: 607
Release:
Genre: Computers
ISBN:

Learn fundamental to advanced GCP architectural techniques using 30 + real-world use cases. The 'Google Cloud Platform an Architect's Guide' is a comprehensive handbook that covers everything that you need to know from GCP fundamentals to advanced cloud architecture topics. The book covers what you need to understand to pass the Google certification exams but goes far further and deeper as it explores real-world use cases and business scenarios. But you don't need to be an IT expert as the book is designed to cater for both beginners and those experienced in other cloud or on other on-premises networks. To that end, the book is split into distinct parts that caters for all levels of expertise. Part -1 is aimed at the novice someone new to a cloud architecture environment that needs to become familiar with the fundamentals of cloud architecture and industry best practices so the more experienced reader may wish to skip this section. Part-2 takes a far deeper dive into GCP theory and practice as well as providing real-world use cases and practical tips that are beneficial for architects at all levels. Part-3 delves much deeper into GCP practical theory on elasticity, scalability and resilience. It also covers Kubernetes in greater detail and touches on High-Performance Computing and IoT designs. The book closes with a final part dealing with cloud-native design practices and as such it covers design, monitoring, notification and remediation techniques to ensure best practice in cloud-native application design, deployment, stabilisation and commissioning.

Data Science on the Google Cloud Platform

Data Science on the Google Cloud Platform
Author: Valliappa Lakshmanan
Publisher: "O'Reilly Media, Inc."
Total Pages: 403
Release: 2017-12-12
Genre: Computers
ISBN: 1491974532

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you’ll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You’ll learn how to: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines

Google Certification Guide - Google Professional Machine Learning Engineer

Google Certification Guide - Google Professional Machine Learning Engineer
Author: Cybellium Ltd
Publisher: Cybellium Ltd
Total Pages: 171
Release:
Genre: Computers
ISBN:

Google Certification Guide - Google Professional Machine Learning Engineer Unlock the World of Machine Learning on Google Cloud Embark on a transformative journey to become a Google Professional Machine Learning Engineer with this comprehensive guide. Designed for those who aspire to master the application of machine learning techniques and tools in the Google Cloud environment, this book is an essential resource for professionals seeking to harness the power of ML in their projects and workflows. What Awaits Inside: Advanced ML Concepts and Practices: Dive deep into the world of machine learning on Google Cloud, covering services like AI Platform, TensorFlow, and BigQuery ML. Real-World Applications: Learn through practical scenarios and hands-on examples, illustrating the effective implementation of machine learning models and solutions on Google Cloud. Strategic Exam Preparation: Gain crucial insights into the certification exam's structure and content, complemented by comprehensive practice questions and preparation strategies. Cutting-Edge ML Trends: Stay updated with the latest advancements in Google Cloud machine learning technologies, ensuring your skills remain relevant and innovative. Authored by a Machine Learning Expert Written by an experienced practitioner in the field of machine learning on Google Cloud, this guide bridges the gap between theoretical knowledge and practical application, offering a rich and comprehensive learning experience. Your Comprehensive Guide to ML Certification Whether you’re an experienced machine learning engineer or looking to elevate your expertise in Google Cloud's ML offerings, this book is a valuable companion, guiding you through the intricacies of machine learning in Google Cloud and preparing you for the Professional Machine Learning Engineer certification. Elevate Your Machine Learning Journey This guide is more than a pathway to certification; it's a deep dive into the practical and innovative aspects of machine learning in the Google Cloud environment, designed to equip you with the skills and knowledge for a thriving career in this dynamic field. Begin Your Machine Learning Adventure Start your journey to becoming a certified Google Professional Machine Learning Engineer. This guide is not just about passing an exam; it's about unlocking new opportunities and frontiers in the exciting world of machine learning on Google Cloud. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com