Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications

Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications
Title Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications PDF eBook
Author David Linthicum
Publisher
Pages
Release 2020
Genre
ISBN

Download Leveraging Cloud-Based Machine Learning on Google Cloud Platform: Real World Applications Book in PDF, Epub and Kindle

In order to successfully leverage AI on Google Cloud Platform (GCP), you must understand what AI is and become familiar with the native tools that GCP offers. This practical course takes you through the basics of leveraging GCP for AI-based applications, including the tools that you can leverage today and how to use them correctly. Instructor David Linthicum introduces Vision AI, a key image identification product from Google, as well as Kubeflow, the machine learning (ML) toolkit designed to simplify the process of deploying ML workflows on Kubernetes. Throughout the course, David presents a variety of real-world use cases that illustrate how these concepts work in practice.

Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications

Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications
Title Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications PDF eBook
Author
Publisher
Pages
Release 2019
Genre
ISBN

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In order to successfully incorporate AI on the popular Azure platform, you must gain a fundamental understanding of what AI is and become familiar with the local tools Azure offers. In this course, David Linthicum covers the basics of leveraging Azure for AI-based applications, including key tools and the processes for using them correctly. After going over the basics of AI processing on Azure, creating knowledge bases, and the use of AI systems in the cloud, David presents real-world use cases across a variety of industries, including healthcare, finance, law enforcement, and manufacturing. He then shows how to work with the Azure Machine Learning (AML) cloud service to build, train, and deploy machine learning models; leverage the Azure Search (AS) tool; and build an AML application.

Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications

Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications
Title Leveraging Cloud-Based Machine Learning on AWS: Real-World Applications PDF eBook
Author
Publisher
Pages
Release 2019
Genre
ISBN

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The cost and efficiency of the cloud puts machine learning and artificial intelligence (AI) within the grasp of enterprises big and small. Help your organization tap into their power with Amazon Web Services. This course is a practical approach to leveraging AWS for AI-based applications across a variety of industries, including healthcare, finance, law enforcement, manufacturing, and education. Instructor David Linthicum introduces SageMaker, Amazon's AI platform, and presents a variety of use cases that demonstrate current best practices, tools, and techniques. He shows how to build and train machine learning models with SageMaker, and integrate them into real-world apps. David also dispels some concerns around AI, such as cost and security, by showcasing real AWS solutions.

Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications

Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications
Title Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications PDF eBook
Author
Publisher
Pages
Release 2019
Genre
ISBN

Download Leveraging Cloud-Based Machine Learning on Azure: Real-World Applications Book in PDF, Epub and Kindle

Learn the basics of leveraging Microsoft Azure for AI-based applications, including key tools and the processes for using them correctly.

The Definitive Guide to Google Vertex AI

The Definitive Guide to Google Vertex AI
Title The Definitive Guide to Google Vertex AI PDF eBook
Author Jasmeet Bhatia
Publisher Packt Publishing Ltd
Pages 422
Release 2023-12-29
Genre Computers
ISBN 1801813329

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Implement machine learning pipelines with Google Cloud Vertex AI Key Features Understand the role of an AI platform and MLOps practices in machine learning projects Get acquainted with Google Vertex AI tools and offerings that help accelerate the creation of end-to-end ML solutions Implement Vision, NLP, and recommendation-based real-world ML models on Google Cloud Platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWhile AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management. This book is a comprehensive guide that lets you explore Google Vertex AI’s easy-to-advanced level features for end-to-end ML solution development. Throughout this book, you’ll discover how Vertex AI empowers you by providing essential tools for critical tasks, including data management, model building, large-scale experimentations, metadata logging, model deployments, and monitoring. You’ll learn how to harness the full potential of Vertex AI for developing and deploying no-code, low-code, or fully customized ML solutions. This book takes a hands-on approach to developing u deploying some real-world ML solutions on Google Cloud, leveraging key technologies such as Vision, NLP, generative AI, and recommendation systems. Additionally, this book covers pre-built and turnkey solution offerings as well as guidance on seamlessly integrating them into your ML workflows. By the end of this book, you’ll have the confidence to develop and deploy large-scale production-grade ML solutions using the MLOps tooling and best practices from Google.What you will learn Understand the ML lifecycle, challenges, and importance of MLOps Get started with ML model development quickly using Google Vertex AI Manage datasets, artifacts, and experiments Develop no-code, low-code, and custom AI solution on Google Cloud Implement advanced model optimization techniques and tooling Understand pre-built and turnkey AI solution offerings from Google Build and deploy custom ML models for real-world applications Explore the latest generative AI tools within Vertex AI Who this book is for If you are a machine learning practitioner who wants to learn end-to-end ML solution development on Google Cloud Platform using MLOps best practices and tools offered by Google Vertex AI, this is the book for you.

Building Machine Learning and Deep Learning Models on Google Cloud Platform

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

Download Building Machine Learning and Deep Learning Models on Google Cloud Platform Book in PDF, Epub and Kindle

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

Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment (English Edition)

Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment (English Edition)
Title Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment (English Edition) PDF eBook
Author Navin Sabharwal
Publisher BPB Publications
Pages 159
Release 2021-01-05
Genre Computers
ISBN 9388511921

Download Up and Running Google AutoML and AI Platform: Building Machine Learning and NLP Models Using AutoML and AI Platform for Production Environment (English Edition) Book in PDF, Epub and Kindle

A step-by-step guide to build machine learning and NLP models using Google AutoML KEY FEATURESÊ ¥Understand the basic concepts of Machine Learning and Natural Language Processing ¥Understand the basic concepts of Google AutoML, AI Platform, and Tensorflow ¥Explore the Google AutoML Natural Language service ¥Understand how to implement NLP models like Issue Categorization Systems using AutoML ¥Understand how to release the features of AutoML models as REST APIs for other applications ¥Understand how to implement the NLP models using the Google AI Platform DESCRIPTIONÊÊ Google AutoML and AI Platform provide an innovative way to build an AI-based system with less effort. In this book, you will learn about the basic concepts of Machine Learning and Natural Language Processing. You will also learn about the Google AI services such as AutoML, AI Platform, and Tensorflow, GoogleÕs deep learning library, along with some practical examples using these services in real-life scenarios. You will also learn how the AutoML Natural Language service and AI Platform can be used to build NLP and Machine Learning models and how their features can be released as REST APIs for other applications. In this book, you will also learn the usage of GoogleÕs BigQuery, DataPrep, and DataProc for building an end-to-end machine learning pipeline. Ê This book will give you an in-depth knowledge of Google AutoML and AI Platform by implementing real-life examples such as the Issue Categorization System, Sentiment Analysis, and Loan Default Prediction System. This book is relevant to the developers, cloud enthusiasts, and cloud architects at the beginner and intermediate levels. WHAT YOU WILL LEARNÊ By the end of this book, you will learn how Google AutoML, AI Platform, BigQuery, DataPrep, and Dapaproc can be used to build an end-to-end machine learning pipeline. You will also learn how different types of AI problems can be solved using these Google AI services. A step-by-step implementation of some common NLP problems such as the Issue Categorization System and Sentiment Analysis System that provide you with hands-on experience in building complex AI-based systems by easily leveraging the GCP AI services. Ê WHO IS THIS BOOK FORÊ This book is for machine learning engineers, NLP users, and data professionals who want to develop and streamline their ML models and put them into production using Google AI services. Prior knowledge of python programming and the basics of machine learning would be preferred. TABLE OF CONTENTS 1. Introduction to Artificial Intelligence 2. Introducing the Google Cloud Platform 3. AutoML Natural Language 4. Google AI Platform 5. Google Data Analysis, Preparation, and Processing Services AUTHOR BIOÊ Navin Sabharwal: Navin is an innovator, leader, author, and consultant in AI and Machine Learning, Cloud Computing, Big Data Analytics, Software Product Development, Engineering, and R&D. He has authored books on technologies such as GCP, AWS, Azure, AI and Machine Learning systems, IBM Watson, chef, GKE, Containers, and Microservices. He is reachable at [email protected]. Amit Agrawal: Amit holds a masterÕs degree in Computer Science and Engineering from MNNIT (Motilal Nehru National Institute of Technology, Allahabad), one of the premier institutes of Engineering in India. He is working as a principal Data Scientist and researcher, delivering solutions in the fields of AI and Machine Learning. He is responsible for designing end-to-end solutions and architecture for enterprise products. He is reachable at [email protected].