Data Engineering for Machine Learning Pipelines

Author	: Pavan Kumar Narayanan
Publisher	: Springer Nature
Total Pages	: 651
Release	:
ISBN-10	: 9798868806025
ISBN-13	:
Rating	: 4/5 ( Downloads)

DOWNLOAD EBOOK

Book Synopsis Data Engineering for Machine Learning Pipelines by : Pavan Kumar Narayanan

Download or read book Data Engineering for Machine Learning Pipelines written by Pavan Kumar Narayanan and published by Springer Nature. This book was released on with total page 651 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Engineering for Machine Learning Pipelines Related Books

Language: en
Pages: 277

Data Pipelines Pocket Reference

Authors: James Densmore

Categories: Computers

Type: BOOK - Published: 2021-02-10 - Publisher: O'Reilly Media

DOWNLOAD EBOOK

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the differe

Language: en
Pages: 358

Building Machine Learning Pipelines

Authors: Hannes Hapke

Categories: Computers

Type: BOOK - Published: 2020-07-13 - Publisher: "O'Reilly Media, Inc."

DOWNLOAD EBOOK

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Ha

Language: en
Pages: 651

Data Engineering for Machine Learning Pipelines

Authors: Pavan Kumar Narayanan

Categories:

Type: BOOK - Published: - Publisher: Springer Nature

DOWNLOAD EBOOK

Language: en
Pages: 563

Deep Learning Pipeline

Authors: Hisham El-Amir

Categories: Computers

Type: BOOK - Published: 2019-12-20 - Publisher: Apress

DOWNLOAD EBOOK

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeli

Language: en
Pages: 234

Data Science in Production

Authors: Ben Weber

Categories:

Type: BOOK - Published: 2020 - Publisher:

DOWNLOAD EBOOK

Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and dep