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Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
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About Data Science On AWS: Implementing
Product Description With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more Review "Wow--this book will help you to bring your data science projects from idea all the wayto production. Chris and Antje have covered all of the important concepts and thekey AWS services, with plenty of real-world examples to get you startedon your data science journey."--Jeff Barr,Vice President & Chief Evangelist,Amazon Web Services "It's very rare to find a book that comprehensively covers the full end-to-end process ofmodel development and deployment! If you're an ML practitioner, this book is a must!"--Ramine Tinati,Managing Director/Chief Data Scientist Applied Intelligence,Accenture "This book is a great resource for building scalable machine learning solutions on AWScloud. It includes best practices for all aspects of model building, including training,deployment, security, interpretability, and MLOps."--Geeta Chauhan,AI/PyTorch Partner Engineering Head,Facebook AI "The landscape of tools on AWS for data scientists and engineers can be absolutelyoverwhelming. Chris and Antje have done the community a service by providing a mapthat practitioners can use to orient themselves, find the tools they need to get thejob done and build new systems that bring their ideas to life."--Josh Wills,Author, Advanced Analytics with Spark (O'Reilly) "Successful data science teams know that data science isn't just modeling but needs adisciplined approach to data and production deployment. We have an army of tools for allof these at our disposal in major clouds like AWS. Practitioners will appreciate thiscomprehensive, practical field guide that demonstrates not just how to applythe tools but which ones to use and when."--Sean Owen,Principal Solutions Architect,Databricks From the Author With this practical book, AI and machine learning (ML) practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services (AWS). The Amazon AI and ML stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. * Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more. * Use automated ML (AutoML) to implement a specific subset of use cases with Amazon SageMaker Autopilot. * Dive deep into the complete model development life cycle for a BERT-based natural language processing
