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Show Notes
Data Science on AWS (Chris Fregly)
- Amazon USA Store: https://www.amazon.com/dp/1492079391?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Science-on-AWS-Chris-Fregly.html
- Apple Books: https://books.apple.com/us/audiobook/data-science-on-aws-leveraging-aws-for-advanced-data/id1753901899?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Science+on+AWS+Chris+Fregly+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492079391/
#AmazonSageMaker #MLOpspipelines #SageMakerAutopilot #Kinesisstreaminganalytics #AWSidentityandaccessmanagement #DataScienceonAWS
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines is a technical guide to operationalizing machine learning with the AWS ecosystem. Written by Chris Fregly and Antje Barth, it treats data science not as an isolated modeling exercise but as a production discipline spanning data ingestion, preparation, training, deployment, monitoring, security, and automation. The book is organized around practical cloud implementation, with Amazon SageMaker serving as a central platform and supporting AWS data, streaming, storage, and identity services supplying the surrounding infrastructure. Its examples extend beyond generic tabular modeling to areas such as BERT-based natural language processing, computer vision, fraud detection, conversational interfaces, and real-time analytics. The central purpose is to show how teams can turn experimental notebooks and models into repeatable, continuously delivered systems. Rather than offering a broad survey of machine learning theory, it concentrates on architectural decisions, service integration, and the operational controls needed to run ML workloads reliably at scale.
- Amazon USA Store: https://www.amazon.com/dp/1492079391?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Science-on-AWS-Chris-Fregly.html
- Apple Books: https://books.apple.com/us/audiobook/data-science-on-aws-leveraging-aws-for-advanced-data/id1753901899?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Science+on+AWS+Chris+Fregly+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1492079391/
#AmazonSageMaker #MLOpspipelines #SageMakerAutopilot #Kinesisstreaminganalytics #AWSidentityandaccessmanagement #DataScienceonAWS
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines is a technical guide to operationalizing machine learning with the AWS ecosystem. Written by Chris Fregly and Antje Barth, it treats data science not as an isolated modeling exercise but as a production discipline spanning data ingestion, preparation, training, deployment, monitoring, security, and automation. The book is organized around practical cloud implementation, with Amazon SageMaker serving as a central platform and supporting AWS data, streaming, storage, and identity services supplying the surrounding infrastructure. Its examples extend beyond generic tabular modeling to areas such as BERT-based natural language processing, computer vision, fraud detection, conversational interfaces, and real-time analytics. The central purpose is to show how teams can turn experimental notebooks and models into repeatable, continuously delivered systems. Rather than offering a broad survey of machine learning theory, it concentrates on architectural decisions, service integration, and the operational controls needed to run ML workloads reliably at scale.
