Getting Started With Aws Cloud Essentials
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Python Essentials for AWS Cloud Developers
Author: Serkan Sakinmaz
language: en
Publisher: Packt Publishing Ltd
Release Date: 2023-05-19
A comprehensive guide to implementing Python applications in AWS while learning about key AWS services Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain hands-on experience in AWS services to effectively implement Python programming Utilize Python with open source libraries to develop data pipelines, APIs, and database applications Leverage the power of AWS to create a cloud-based server and use monitoring and logging features Book Description AWS provides a vast variety of services for implementing Python applications, which can pose a challenge for those without an AWS background. This book addresses one of the more predominant problems of choosing the right service and stepping into the implementation of exciting Python apps using AWS. The book begins by showing you how to install Python and create an AWS account, before helping you explore AWS Lambda, EC2, Elastic Beanstalk, and S3 for Python programming. You'll then gain hands-on experience in using these services to build the Python application. As you advance, you'll discover how to debug Python apps using PyCharm, and then start deploying the Python applications on Elastic Beanstalk. You'll also learn how to monitor Python applications using the CloudWatch service, along with creating and publishing APIs on AWS to access the Python application. The concluding chapters will help you get to grips with storing unstructured and semi-structured data using NoSQL and DynamoDB, as well as advance your knowledge using the Glue serverless data integration service in AWS. By the end of this Python book, you'll be able to take your application development skills up a notch with AWS services and advance in your career. What you will learn Understand the fundamentals of AWS services for Python programming Find out how to configure AWS services to build Python applications Run and deploy Python applications using Lambda, EC2, and Elastic Beanstalk Provision EC2 servers on AWS and run Python applications Debug and monitor Python applications using PyCharm and CloudWatch Understand database operations on AWS by learning about DynamoDB and RDS Explore the API gateway service on AWS using Python to grasp API programming Who this book is for This book is for cloud developers, software developers, and IT specialists who want to develop Python applications on AWS as well as learn the concepts underlying AWS services for implementing the applications. Experience in Python programming is needed to be able to implement the applications on AWS.
AWS CLI Essentials
"AWS CLI Essentials: A Beginner's Guide to Cloud Automation" is your comprehensive resource for mastering the AWS Command Line Interface and harnessing its powerful capabilities to manage and automate AWS services. Tailored for newcomers, this guide meticulously demystifies complex concepts, offering step-by-step instructions and practical insights essential for navigating the cloud landscape. Beginning with foundational knowledge of cloud computing and AWS, the book progressively delves into configuring the AWS CLI, executing basic to advanced commands, and leveraging automation to streamline operations. The book emphasizes hands-on learning with detailed examples and best practice recommendations, empowering readers to efficiently manage AWS resources, optimize workflows, and enhance operational security. By integrating CLI techniques with scripting, users gain the expertise to automate routine tasks, ensuring robust and reliable AWS environments. Accompanied by troubleshooting strategies and support resources, "AWS CLI Essentials" provides the tools to confidently address challenges and maximize the benefits of AWS cloud technology in your organizational pursuits.
Getting Started with Amazon SageMaker Studio
Author: Michael Hsieh
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-03-31
Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code Key FeaturesUnderstand the ML lifecycle in the cloud and its development on Amazon SageMaker StudioLearn to apply SageMaker features in SageMaker Studio for ML use casesScale and operationalize the ML lifecycle effectively using SageMaker StudioBook Description Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment. In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio. By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases. What you will learnExplore the ML development life cycle in the cloudUnderstand SageMaker Studio features and the user interfaceBuild a dataset with clicks and host a feature store for MLTrain ML models with ease and scaleCreate ML models and solutions with little codeHost ML models in the cloud with optimal cloud resourcesEnsure optimal model performance with model monitoringApply governance and operational excellence to ML projectsWho this book is for This book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.