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

Chris Fregly

ISBN 10: 1492079391 / ISBN 13: 9781492079392

Verlag: O'reilly Media Mai 2021, 2021

Beschreibung

Neuware - If you use data to make critical business decisions, this book is for you. Whether you're a data analyst, research scientist, data engineer, ML engineer, data scientist, application developer, or systems developer, this guide helps you broaden your understanding of the modern data science stack, create your own machine learning pipelines, and deploy them to applications at production scale.

Bestandsnummer des Verkäufers 9781492079392

Bibliografische Details

• Titel: Data Science on AWS : Implementing End-To-End, Continuous AI and Machine Learning Pipelines
• Verlag: O'reilly Media Mai 2021
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 1492079391
• ISBN-13: 9781492079392
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 233B x 174 H x 31 T Millimeter Breite: 233 mm, Höhe: 174 mm, Tiefe: 31 mm
• Gewicht: 891 Gramm

Über diesen Titel

Inhaltsangabe

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

Über die Autorin bzw. den Autor

Chris Fregly is a Developer Advocate for AI and Machine Learning at AWS, based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs". Antje Barth is a Developer Advocate for AI and Machine Learning at AWS, based in Dusseldorf, Germany. She is also co-founder of the Dusseldorf chapter of Women in Big Data Meetup. Antje frequently speaks at AI and Machine Learning conferences and meetups around the world, including the O'Reilly AI and Strata conferences. Besides ML/AI, Antje is passionate about helping developers leverage Big Data, container and Kubernetes platforms in the context of AI and Machine Learning. Prior to joining AWS, Antje worked in technical evangelist and solutions engineering roles at MapR and Cisco