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Productionizing AIhow to deliver AI B2B solutions with Cloud and Python /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Productionizing AIby Barry Walsh.
其他題名:	how to deliver AI B2B solutions with Cloud and Python /
作者:	Walsh, Barry.
出版者:	Berkeley, CA :Apress :2023.
面頁冊數:	xxv, 373 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Artificial intelligenceIndustrial applications.
電子資源:	https://doi.org/10.1007/978-1-4842-8817-7
ISBN:	9781484288177$q(electronic bk.)

Productionizing AIhow to deliver AI B2B solutions with Cloud and Python /
Walsh, Barry.

Productionizing AIhow to deliver AI B2B solutions with Cloud and Python /[electronic resource] :by Barry Walsh. - Berkeley, CA :Apress :2023. - xxv, 373 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1: Introduction to AI & the AI Ecosystem -- Chapter 2: AI Best Practise & DataOps -- Chapter 3: Data Ingestion for AI -- Chapter 4: Machine Learning on Cloud -- Chapter 5: Neural Networks and Deep Learning -- Chapter 6: The Employer's Dream: AutoML, AutoAI and the rise of NoLo UIs -- Chapter 7: AI Full Stack: Application Development -- Chapter 8: AI Case Studies -- Chapter 9: Deploying an AI Solution (Productionizing & Containerization) -- Chapter 10: Natural Language Processing -- Postscript.

This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app. From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions. The book is intended for those whose role demands overcoming budgetary barriers or constraints in accessing cloud credits to undertake the often difficult process of developing and deploying an AI solution. You will: Develop and deliver production-grade AI in one month Deploy AI solutions at a low cost Work around Big Tech dominance and develop MVPs on the cheap Create demo-ready solutions without overly complex python scripts/notebooks.

ISBN: 9781484288177$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8817-7doiSubjects--Topical Terms:

524554
Artificial intelligence
--Industrial applications.

LC Class. No.: TA347.A78 / W35 2023

Dewey Class. No.: 006.3

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520 $a This book is a guide to productionizing AI solutions using best-of-breed cloud services with workarounds to lower costs. Supplemented with step-by-step instructions covering data import through wrangling to partitioning and modeling through to inference and deployment, and augmented with plenty of Python code samples, the book has been written to accelerate the process of moving from script or notebook to app. From an initial look at the context and ecosystem of AI solutions today, the book drills down from high-level business needs into best practices, working with stakeholders, and agile team collaboration. From there you'll explore data pipeline orchestration, machine and deep learning, including working with and finding shortcuts using artificial neural networks such as AutoML and AutoAI. You'll also learn about the increasing use of NoLo UIs through AI application development, industry case studies, and finally a practical guide to deploying containerized AI solutions. The book is intended for those whose role demands overcoming budgetary barriers or constraints in accessing cloud credits to undertake the often difficult process of developing and deploying an AI solution. You will: Develop and deliver production-grade AI in one month Deploy AI solutions at a low cost Work around Big Tech dominance and develop MVPs on the cheap Create demo-ready solutions without overly complex python scripts/notebooks.
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