Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

This book provides practical guidance for building and deploying data science projects on Amazon Web Services, covering topics such as cloud pipeline development, automated machine learning, real-time ML, anomaly detection, and security best practices.

Format: Paperback / softback
Length: 400 pages
Publication date: 23 April 2021
Publisher: O'Reilly Media, Inc, USA

This comprehensive guide is designed to empower AI and machine learning practitioners with the skills and knowledge necessary to successfully build and deploy data science projects on Amazon Web Services (AWS). The Amazon AI and machine learning stack, a powerful combination of data science, data engineering, and application development, empowers users to enhance their skills and take their projects to new heights.

In this guide, authors Chris Fregly and Antje Barth take readers on a journey through the process of building and running pipelines in the cloud, seamlessly integrating the results into applications in minutes rather than days. Throughout the book, they showcase practical techniques and strategies to reduce costs and improve performance, making it an invaluable resource for anyone seeking to optimize their AWS operations.

One of the key highlights of this guide is its real-world application focus. Authors demonstrate how to apply the Amazon AI and ML stack to a wide range of use cases, including natural language processing, computer vision, fraud detection, conversational devices, and more. They provide step-by-step instructions and code examples that allow readers to apply these technologies to their own projects, ensuring a practical and hands-on approach.

In addition to its practical focus, the guide also delves deep into the complete model development lifecycle. Readers learn how to ingest data, analyze it, and build powerful machine learning models using popular frameworks such as TensorFlow and PyTorch. They explore advanced topics such as automated machine learning with Amazon SageMaker Autopilot, diving into the process of implementing specific use cases with ease.

The book also covers essential aspects of machine learning operations, including data streaming, anomaly detection, and streaming analytics. Readers learn how to leverage Amazon Kinesis and Managed Streaming for Apache Kafka to process data streams in real-time, enabling them to make timely and informed decisions based on their data. Security is a critical concern in data science projects, and the guide provides comprehensive best practices for ensuring the privacy and integrity of user data.

Whether you are a seasoned AI and machine learning professional or just starting your journey, this guide is an essential resource for anyone seeking to optimize their AWS operations and build successful data science projects. With its comprehensive coverage, practical examples, and expert guidance, it provides a solid foundation for anyone looking to leverage the power of AI and machine learning in their business.

In conclusion, this comprehensive guide to building and deploying data science projects on Amazon Web Services is a must-read for AI and machine learning practitioners. It empowers users with the skills and knowledge necessary to optimize their AWS operations, build powerful machine learning models, and apply these technologies to real-world use cases. Whether you are a seasoned professional or just starting your journey, this guide will help you take your projects to new heights and achieve your goals.

Weight: 886g
Dimension: 179 x 235 x 30 (mm)
ISBN-13: 9781492079392