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

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Ebook About
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 upyour 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 moreUse automated machine learning to implement a specific subset of use cases with SageMaker AutopilotDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deploymentTie everything together into a repeatable machine learning operations pipelineExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache KafkaLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

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



Very well written, this book covers many AWS services across the entire Amazon AI/ML data science stack. After clearly explaining the value proposition of doing data science in the cloud, the authors navigate the reader through an complete end-to-end machine learning pipeline using the latest in natural language processing techniques including BERT, HuggingFace transformers, and Amazon SageMaker. The authors demonstrate how to implement automated pipelines using TensorFlow, PyTorch, MXNet, Python, and even Java! This book has both technical depth and practical breadth. This book helped me prepare for - and complete - my AWS ML Specialty certification. It was a true delight to read!
I have been following this book since it was in beta thanks to an Oreilly subscription. I have also attended a workshop put on by the authors. It has greatly helped my overall understanding of how to practically implement in AWS. You can use this as a framework to figure out how and what services to implement.

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