Getting Started With Aws Glue
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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.
Serverless ETL and Analytics with AWS Glue
Author: Vishal Pathak
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-08-30
Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features Book DescriptionOrganizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes. Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You’ll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you’ll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options. By the end of this AWS book, you’ll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue.What you will learn Apply various AWS Glue features to manage and create data lakes Use Glue DataBrew and Glue Studio for data preparation Optimize data layout in cloud storage to accelerate analytics workloads Manage metadata including database, table, and schema definitions Secure your data during access control, encryption, auditing, and networking Monitor AWS Glue jobs to detect delays and loss of data Integrate Spark ML and SageMaker with AWS Glue to create machine learning models Who this book is for ETL developers, data engineers, and data analysts
AWS Glue for Data Engineers
"AWS Glue for Data Engineers: Serverless ETL Made Easy" is an indispensable resource for data engineers seeking to master the art of efficient data integration and transformation in the cloud. This comprehensive guide provides an in-depth exploration of AWS Glue, a powerful tool that streamlines the extract, transform, and load (ETL) processes. Whether you are a novice or an experienced professional, this book is structured to enhance your understanding, covering everything from setup and configuration to advanced features and integrations with other AWS services. Within its pages, readers will discover seamless ways to optimize workflows, harness the full potential of serverless computing, and ensure robust data security and compliance. The book artfully combines practical insights with best practices, guiding you through the complexities of ETL with clear, step-by-step instructions. With real-world use cases and practical examples, it provides a robust framework for leveraging AWS Glue’s capabilities to drive your data engineering tasks, offering solutions to common challenges faced in modern data ecosystems. "AWS Glue for Data Engineers" is not just a technical manual; it’s a strategic roadmap for data professionals striving to enhance their skills in the rapidly evolving field of cloud computing. By adopting its methodologies, you can optimize your ETL workflows, reduce costs, and increase efficiency. Equip yourself with the knowledge to transform your data management practices and create scalable, dynamic systems that meet today’s business demands. Let this book be your guide to unlocking new efficiencies and innovations in your data engineering journey.