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AWS Penetration Testing
AWS Penetration Testing

Cloud security has always been treated as the highest priority by AWS while designing a robust cloud infrastructure. AWS has now extended its support to allow users and security experts to perform penetration tests on its environment. This has not only revealed a number of loopholes and brought vulnerable points in their existing system to the fore, but has also opened up opportunities for organizations to build a secure cloud environment. This book teaches you how to perform penetration tests in a controlled AWS environment. You'll begin by performing security assessments of major AWS resources such as Amazon EC2 instances, Amazon S3, Amazon API Gateway, and AWS Lambda. Throughout the course of this book, you'll also learn about specific tests such as exploiting applications, compromising Identity and Access Management (IAM) keys, testing permissions flaws, and discovering weak policies. Moving on, you'll discover how to establish private-cloud access through backdoor Lambda functi ...
Kubeflow Operations Guide
Kubeflow Operations Guide

Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads-a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform; Understand the process of planning your Kubeflow deployment; Install Kubeflow on an existing on-premise Kubernetes cluster; Dep ...
Machine Learning for Kids
Machine Learning for Kids

Artificial intelligence (AI) is the ability of computers to simulate human thinking. Machine learning (ML) is one of the building blocks of AI. It's based on the idea that computers can be taught to do things on their own from the data and feedback you give them. Machine Learning for Kids consists of this book and a kid-friendly companion website paired with the educational coding platform, Scratch. Together, they provide an easy-to-use guided programming environment for adding ML capabilities to your own AI projects! As you work through each chapter you'll discover how ML systems can be taught to recognize text, images, numbers, and sounds, and different ways of training ML models to improve their accuracy. You'll turn your models into fun computer games and apps (and see what happens when an AI system gets confused by bad data) while building: - A Rock, Paper, Scissors game that knows your hand shapes; - A smart question-answering chatbot; - A computer character that reacts ...
Python for You and Me
Python for You and Me

This is a simple open book to learn Python programming language, it is for the programmers who are new to Python. Python is an interpreted, high-level and general-purpose programming language. Python consistently ranks as one of the most popular programming languages. Large organizations that use Python include Wikipedia, Google, Yahoo, CERN, NASA, Dropbox, IBM, Mozilla, Facebook, Amazon, Instagram, Spotify, Reddit, ... ...
Advanced Forecasting with Python
Advanced Forecasting with Python

Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, and Amazon's DeepAR model. Rather than focus on a specific set of models, this book presents an exhaustive overview of all the techniques relevant to practitioners of forecasting. It begins by explaining the different categories of models that are relevant for forecasting in a high-level language. Next, it covers univariate and multivariate time series models followed by advanced machine learning and deep learning models. It concludes with reflections on model selection such as benchmark scores vs. understandability of models vs. compute time, and automated retraining and updating of models. Each of the models presented in this book is covered in depth, with an intuitive simple explanation of the model, a m ...
Spring Boot with React and AWS
Spring Boot with React and AWS

Create and deploy full-stack Spring Boot applications with React and AWS. This practical and authoritative guide teaches you the fundamentals of Amazon Web Services with no prior experience. You will start by learning the fundamentals of AWS, including EC2, S3, IAM, and load balancer. Next, you will learn to deploy a Spring Boot REST API to AWS with Elastic Beanstalk, which will give you the ability to build and deploy a Spring Boot application. You will explore the RDS relational database and create an instance of a MySQL database in AWS with RDS. You will then deploy a Spring Boot application to MySQL in AWS and deploy a full-stack Spring Boot React application to AWS using Elastic Beanstalk and S3. Lastly, you will be introduced to ELB, CloudWatch, and Route 53. By the end of this book you will understand full-stack Spring Boot React applications and their deployment. If you prefer "learning by coding" then Spring Boot with React and AWS is the book for you. ...
AWS Cookbook
AWS Cookbook

This practical guide provides over 70 self-contained recipes to help you creatively solve common AWS challenges you'll encounter on your cloud journey. If you're comfortable with rudimentary scripting and general cloud concepts, this cookbook provides what you need to address foundational tasks and create high-level capabilities. Authors John Culkin and Mike Zazon share real-world examples that incorporate best practices. Each recipe includes a diagram to visualize the components. Code is provided so that you can safely execute in an AWS account to ensure solutions work as described. From there, you can customize the code to help construct an application or fix an existing problem. Each recipe also includes a discussion to provide context, explain the approach, and challenge you to explore the possibilities further. Go beyond theory and learn the details you need to successfully build on AWS. The recipes help you: Redact personal identifiable information (PII) from text using Ama ...
Beginning DevOps on AWS for iOS Development
Beginning DevOps on AWS for iOS Development

Apply the principles of DevOps in software development - automated builds, automated tests, and continuous deployment and delivery - to iOS application development on Amazon Web Services (AWS) cloud. This book provides detailed walkthroughs and example source code is provided for the concepts discussed so you can put theory into practical application. You'll start by building a basic iOS application and then dive deep into key AWS Developer tools and services to see how they can be used in iOS application development. Then review the details of the integration of these fully managed AWS services with open-source tools such as Jenkins and Fastlane that allow developers to get the best of both worlds and further help to enhance and increase agility in the development lifecycle of iOS apps. You'll also investigate the Amazon EC2 Mac instance, which allows you to run XCode in a cloud environment for the first time to leverage the flexibility, elasticity, and scale of AWS. It also al ...
Automated Machine Learning on AWS
Automated Machine Learning on AWS

AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services. Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed A ...
Designing APIs with Swagger and OpenAPI
Designing APIs with Swagger and OpenAPI

Designing APIs with Swagger and OpenAPI is a comprehensive guide to designing and describing your first RESTful API using the most widely adopted standards. Following expert instruction from Swagger core contributor Josh Ponelat and API consultant Lukas Rosenstock, you'll spend each chapter progressively expanding the kind of APIs you'll want to build in the real world. You'll utilize OpenAPI and Swagger to help automate your workflow, and free up your time to work on more exciting features. Learn the syntax and structure of OpenAPI definitions, create and iterate on an API design with common tools, and release your API to the public. Create web APIs that customers and developers will love! Using Swagger, a collection of tools for defining and documenting REST APIs, you will build safe, controlled access to your software. And because Swagger implements the vendor-neutral OpenAPI specification, you'll be building to the same standards adopted by Google, Microsoft, and Amazon. Desi ...
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