Mastering Machine Learning On Aws
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Mastering Machine Learning on AWS
Author: Dr. Saket S.R. Mengle
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-05-20
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Mastering Machine Learning on AWS
Author: Dr. Saket S.R. Mengle
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-05-20
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Mastering AWS Serverless
Master the art of designing and creating serverless architectures and applications KEY FEATURES ● Learn to create serverless applications that leverage serverless functions, databases, data stores, and application programming interfaces. ● Learn the serverless concepts needed to provide serverless solutions for websites, mobile apps, APIs, backends, notifications, Artificial Intelligence, and Machine Learning. ● Create serverless, event-driven architectures and designs through hands-on exercises throughout the book. DESCRIPTION Serverless computing is relatively new compared to server-based designs. Amazon Web Services launched its serverless computing offering by introducing AWS Lambda. Lambda has introduced a revolution in cloud computing, where servers could be excluded from architectures, and events could be used to trigger other resources. The AWS serverless services have allowed developers, startups, and large enterprises to focus more on developing and creating features and spend less time managing and securing servers. It covers key concepts like serverless architecture and AWS services. You will learn to create event-driven apps, launch websites, and build APIs with hands-on exercises. The book will explore storage options and data processing, including serverless Machine Learning. Discover best practices for architecture, security, and cost optimization. The book will cover advanced topics like AWS SAM and Lambda layers for complex workflows. Finally, get guidance on creating new serverless apps and migrating existing ones. The knowledge gained from this book will help you create a serverless website, application programming interface, and backend. In addition, the information covered in the book will help you process and analyze data using a serverless design. WHAT YOU WILL LEARN ● Creating a serverless website using Amazon S3 and CloudFront. ● Creating a serverless API using Amazon API Gateway. ● Create serverless functions with AWS Lambda. ● Save data using Amazon DynamoDB and Amazon S3. ● Perform authentication and authorization with Amazon Cognito. WHO THIS BOOK IS FOR The book targets professionals and students who want to gain experience in software development, cloud computing, web development, data processing, or Amazon Web Services. It is ideal for cloud architects, developers, and backend engineers seeking to leverage serverless services for scalable and cost-effective applications. TABLE OF CONTENTS 1. Introduction to AWS Serverless 2. Overview of Serverless Applications 3. Designing Serverless Architectures 4. Launching a Website 5. Creating an API 6. Saving and Using Data 7. Adding Authentication and Authorization 8. Processing Data using Automation and Machine Learning 9. Sending Notifications 10. Additional Automation Topics 11. Architecture Best Practices 12. Next Steps