Learning Amazon Web Services (AWS)

Author: Mark Wilkins
Publisher: Addison-Wesley Professional
ISBN: 0135301092
Category : Computers
Languages : en
Pages : 606

Book Description
The Practical, Foundational Technical Introduction to the World’s #1 Cloud Platform Includes access to several hours of online training video: Mark Wilkins’ expert training video library guides you through setting up core services and prepares you to deploy your own apps and resources. Learning Amazon Web Services (AWS) is the perfect foundational resource for all administrators, developers, project managers, and other IT professionals who want to plan and deploy AWS services and/or earn AWS certification. Top cloud trainer and evangelist Mark Wilkins teaches best practices that align with Amazon’s Well-Architected Framework, introduces key concepts in the context of a running case study, carefully explains how core AWS services operate and integrate, and offers extensively tested tips for maximizing flexibility, security, and value. Companion online videos guide you step-by-step through setting AWS compute, storage, networking, scale, security, automation, and more. Balance cost, compliance, and latency in your service designs Choose the right networking options for your virtual private cloud (VPC) Build, host, launch, manage, and budget for EC2 compute services Plan for scale and resiliency, and make informed decisions about AWS storage Enforce strict security, and automate to improve operational efficiency This book with companion training videos is a valuable learning tool for anyone seeking to demonstrate expertise through formal certification. WEB EDITION: All buyers of the book or ebook can register your book for access to a free online Web Edition of this title, which included videos embedded within the text, plus updates as they become available.

Learn Amazon Web Services - simpleNeasyBook by WAGmob

Author: WAGmob
Publisher: WAGmob
ISBN:
Category :
Languages : en
Pages : 388

Book Description
*****WAGmob: An ebook and app platform for learning, teaching and training !!!***** WAGmob brings you, simpleNeasy, on-the-go learning ebook for "Learn Amazon Web Services". The ebook provides: 1. Snack sized chapters for easy learning. 2. Bite sized flashcards to memorize key concepts. Designed for both students and adults. This ebook provides a quick summary of essential concepts in Amazon Web Services by following snack sized chapters: (Each chapter has corresponding flashcards) Introduction to Amazon Web Services: • Cloud Computing • Cloud Computing Stack • Amazon Web Services (AWS) • AWS Products and Services • AWS Uses • AWS Geo-Locations • AWS Management Console • Benefits of AWS AWS Products and Services: • AWS Products and Services Getting Started with Free Usage Tier: • Getting Started with AWS Free Usage Tier • Getting Started with the Free Usage Tier • Compute & Networking Free Tier Benefits • Storage Free Tier Benefits • Database Free Tier Benefits • Application Services Free Tier Benefits • Development and Management Free Tier Benefits Amazon EC2 - I: • Amazon EC2 • History of EC2 • Amazon EC2 Instances • Instance Types and Families • Amazon EBS • Instance Features • Benefits of Amazon EC2 • Amazon EC2 Service Commitment • Differences between Amazon EC2 and Azure? • Amazon EC2 Billing Amazon EC2 - II: • How to Launch an EC2 Instance • How to Connect to an EC2 Instance • How to Terminate an EC2 Instance Amazon S3: • Amazon Simple Storage Service (Amazon S3) • Amazon S3 Functionality • Notable Amazon S3 Users • Amazon S3 Security • Amazon S3 Access Control • Encryption in Amazon S3 • S3 Durability • S3 Versioning • Reduced Redundancy Storage (RSS) Amazon Glacier: • Amazon Glacier • How to Use Glacier? • Amazon Glacier Costing Amazon Account and MFA: • AWS Account and MFA • AWS MFA • Using an AWS MFA Device • AWS MFA Devices • Amazon Single Sign-On Amazon IAM: • AWS Identity and Access Management • IAM Concepts • IAM Policies • EC2 Resource-Level Permission Amazon RDS: • Amazon RDS • RDS Components • Available RDS Interfaces • RDS Commands Cheat Sheet Amazon Marketplace: • AWS Marketplace • AWS Marketplace Products • Benefits of AWS Marketplace • Best Practices for Building AMIs for the Marketplace About WAGmob ebooks: 1) A companion ebook for on-the-go, bite-sized learning. 2) Over One million paying customers from 175+ countries. Why WAGmob ebooks: 1) Beautifully simple, Amazingly easy, Massive selection of ebook. 2) Effective, Engaging and Entertaining ebook. 3) An incredible value for money. Lifetime of free updates! 4) Proven track record with over a million paying customers. ******** WAGmob Vision : simpleNeasy ebooks for a lifetime of on-the-go learning. WAGmob Mission : A simpleNeasy WAGmob ebook in every hand. WAGmob Platform: A unique platform to create and publish your own ebooks and apps. ********* Please visit us at www.simpleNeasyBook.Com or write to us at [email protected]. We would love to improve our ebook and ebook platform.

Learn Amazon Web Services in a Month of Lunches

Author: David Clinton
Publisher: Simon and Schuster
ISBN: 1638351333
Category : Computers
Languages : en
Pages : 397

Book Description
Summary Learn Amazon Web Services in a Month of Lunches guides you through the process of building a robust and secure web application using the core AWS services you really need to know. You'll be amazed by how much you can accomplish with AWS! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Cloud computing has transformed the way we build and deliver software. With the Amazon Web Services cloud platform, you can trade expensive glass room hardware and custom infrastructure for virtual servers and easy-to-configure storage, security, and networking services. Better, because you don't own the hardware, you only pay for the computing power you need! Just learn a few key ideas and techniques and you can have applications up and running in AWS in minutes. About the Book Learn Amazon Web Services in a Month of Lunches gets you started with AWS fast. In just 21 bite-size lessons, you'll learn the concepts and practical techniques you need to deploy and manage applications. You'll learn by doing real-world labs that guide you from the core AWS tool set through setting up security and storage and planning for growth. You'll even deploy a public-facing application that's highly available, scalable, and load balanced. What's Inside First steps with AWS - no experience required Deploy web apps using EC2, RDS, S3, and Route 53 Cheap and fast system backups Setting up cloud automation About the Reader If you know your way around Windows or Linux and have a basic idea of how web applications work, you're ready to start using AWS. About the Author David Clinton is a system administrator, teacher, and writer. He has administered, written about, and created training materials for many important technology subjects including Linux systems, cloud computing (AWS in particular), and container technologies like Docker. Many of his video training courses can be found on Pluralsight.com, and links to his other books (on Linux administration and server virtualization) can be found at https://bootstrap-it.com. Table of Contents Before you begin PART 1 - THE CORE AWS TOOLS The 10-minute EC2 web server Provisioning a more robust EC2 website Databases on AWS DNS: whatâ€™s in a name? S3: cheap, fast file storage S3: cheap, fast system backups AWS security: working with IAM users, groups, and roles Managing growth Pushing back against the chaos: using resource tags CloudWatch: monitoring AWS resources for fun and profit Another way to play: the command-line interface PART 2 - THE AWS POWER USER: OPTIMIZING YOUR INFRASTRUCTURE Keeping ahead of user demand High availability: working with AWS networking tools High availability: load balancing High availability: auto scaling High availability: content-delivery networks PART 3 - FOOD FOR THOUGHT: WHAT ELSE CAN AWS DO FOR YOU? Building hybrid infrastructure Cloud automation: working with Elastic Beanstalk, Docker, and Lambda Everything else (nearly) Never the end

Machine Learning for iOS Developers

Author: Abhishek Mishra
Publisher: John Wiley & Sons
ISBN: 1119602874
Category : Computers
Languages : en
Pages : 352

Book Description
Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.

Networking All-in-One For Dummies

Author: Doug Lowe
Publisher: John Wiley & Sons
ISBN: 1119689015
Category : Computers
Languages : en
Pages : 1059

Book Description
Your ultimate one-stop networking reference Designed to replace that groaning shelf-load of dull networking books you’d otherwise have to buy and house, Networking All-in-One For Dummies covers all the basic and not-so-basic information you need to get a network up and running. It also helps you keep it running as it grows more complicated, develops bugs, and encounters all the fun sorts of trouble you expect from a complex system. Ideal both as a starter for newbie administrators and as a handy quick reference for pros, this book is built for speed, allowing you to get past all the basics—like installing and configuring hardware and software, planning your network design, and managing cloud services—so you can get on with what your network is actually intended to do. In a friendly, jargon-free style, Doug Lowe—an experienced IT Director and prolific tech author—covers the essential, up-to-date information for networking in systems such as Linux and Windows 10 and clues you in on best practices for security, mobile, and more. Each of the nine minibooks demystifies the basics of one key area of network management. Plan and administrate your network Implement virtualization Get your head around networking in the Cloud Lock down your security protocols The best thing about this book? You don’t have to read it all at once to get things done; once you’ve solved the specific issue at hand, you can put it down again and get on with your life. And the next time you need it, it’ll have you covered.

Machine Learning in Production

Author: Suhas Pote
Publisher: BPB Publications
ISBN: 9355518102
Category : Computers
Languages : en
Pages : 463

Book Description
Deploy, manage, and scale Machine Learning models with MLOps effortlessly KEY FEATURES ● Explore several ways to build and deploy ML models in production using an automated CI/CD pipeline. ● Develop and convert ML apps into Android and Windows apps. ● Learn how to implement ML model deployment on popular cloud platforms, including Azure, GCP, and AWS. DESCRIPTION ‘Machine Learning in Production’ is an attempt to decipher the path to a remarkable career in the field of MLOps. It is a comprehensive guide to managing the machine learning lifecycle from development to deployment, outlining ways in which you can deploy ML models in production. It starts off with fundamental concepts, an introduction to the ML lifecycle and MLOps, followed by comprehensive step-by-step instructions on how to develop a package for ML code from scratch that can be installed using pip. It then covers MLflow for ML life cycle management, CI/CD pipelines, and shows how to deploy ML applications on Azure, GCP, and AWS. Furthermore, it provides guidance on how to convert Python applications into Android and Windows apps, as well as how to develop ML web apps. Finally, it covers monitoring, the critical topic of machine learning attacks, and A/B testing. With this book, you can easily build and deploy machine learning solutions in production. WHAT YOU WILL LEARN ● Master the Machine Learning lifecycle with MLOps. ● Learn best practices for managing ML models at scale. ● Streamline your ML workflow with MLFlow. ● Implement monitoring solutions using whylogs, WhyLabs, Grafana, and Prometheus. ● Use Docker and Kubernetes for ML deployment. WHO THIS BOOK IS FOR Whether you are a Data scientist, ML engineer, DevOps professional, Software engineer, or Cloud architect, this book will help you get your machine learning models into production quickly and efficiently. TABLE OF CONTENTS 1. Python 101 2. Git and GitHub Fundamentals 3. Challenges in ML Model Deployment 4. Packaging ML Models 5. MLflow-Platform to Manage the ML Life Cycle 6. Docker for ML 7. Build ML Web Apps Using API 8. Build Native ML Apps 9. CI/CD for ML 10. Deploying ML Models on Heroku 11. Deploying ML Models on Microsoft Azure 12. Deploying ML Models on Google Cloud Platform 13. Deploying ML Models on Amazon Web Services 14. Monitoring and Debugging 15. Post-Productionizing ML Models

Cloud Based Machine Learning – Practical Guide to Deploying AI Models in the Cloud

Author: Hemanth Volikatla
Publisher: RK Publication
ISBN: 8197364567
Category : Computers
Languages : en
Pages : 301

Book Description
Cloud-Based Machine Learning – Practical Guide to Deploying AI Models in the Cloud is a comprehensive resource designed to help professionals and enthusiasts harness the power of cloud platforms for AI deployment. It's key concepts, tools, and techniques for building, training, and deploying machine learning models using services like AWS, Azure, and Google Cloud. With practical examples, step-by-step instructions, and best practices, this guide empowers readers to scale AI solutions efficiently, ensuring robust performance and seamless integration into real-world applications. Perfect for beginners and experts aiming to advance their skills in cloud-based AI technologies.

Learn Amazon SageMaker

Author: Julien Simon
Publisher: Packt Publishing Ltd
ISBN: 1801814155
Category : Computers
Languages : en
Pages : 554

Book Description
Swiftly build and deploy machine learning models without managing infrastructure and boost productivity using the latest Amazon SageMaker capabilities such as Studio, Autopilot, Data Wrangler, Pipelines, and Feature Store Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerOptimize the accuracy, cost, and fairness of your modelsCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Book Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning models at scale without managing any infrastructure. It helps you focus on the machine learning problem at hand and deploy high-quality models by eliminating the heavy lifting typically involved in each step of the ML process. This second edition will help data scientists and ML developers to explore new features such as SageMaker Data Wrangler, Pipelines, Clarify, Feature Store, and much more. You'll start by learning how to use various capabilities of SageMaker as a single toolset to solve ML challenges and progress to cover features such as AutoML, built-in algorithms and frameworks, and writing your own code and algorithms to build ML models. The book will then show you how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how automating your workflows can help you get to production faster with minimum effort and at a lower cost. Finally, you'll explore SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnBecome well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and natural language processing (NLP) models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. A solid understanding of machine learning concepts and the Python programming language will also be beneficial.

Machine Learning in the AWS Cloud

Author: Abhishek Mishra
Publisher: John Wiley & Sons
ISBN: 1119556716
Category : Computers
Languages : en
Pages : 528

Book Description
Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You’ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you’ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. • Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building • Discover common neural network frameworks with Amazon SageMaker • Solve computer vision problems with Amazon Rekognition • Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.

Deep Learning and the Game of Go

Author: Kevin Ferguson
Publisher: Simon and Schuster
ISBN: 1638354014
Category : Computers
Languages : en
Pages : 611

Book Description
Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning