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Machine Learning On Kubernetes A Practical Handbook For Building And Using A Complete Open Source Machine Learning Platform On Kubernetes Masood

  • SKU: BELL-56267644
Machine Learning On Kubernetes A Practical Handbook For Building And Using A Complete Open Source Machine Learning Platform On Kubernetes Masood
$ 31.00 $ 45.00 (-31%)

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Machine Learning On Kubernetes A Practical Handbook For Building And Using A Complete Open Source Machine Learning Platform On Kubernetes Masood instant download after payment.

Publisher: Packt Publishing
File Extension: PDF
File size: 15.13 MB
Pages: 384
Author: Masood, Faisal, Brigoli, Ross
ISBN: 9781803241807, 1803241802
Language: English
Year: 2022

Product desciption

Machine Learning On Kubernetes A Practical Handbook For Building And Using A Complete Open Source Machine Learning Platform On Kubernetes Masood by Masood, Faisal, Brigoli, Ross 9781803241807, 1803241802 instant download after payment.

Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologies Key Features: Build a complete machine learning platform on Kubernetes Improve the agility and velocity of your team by adopting the self-service capabilities of the platform Reduce time-to-market by automating data pipelines and model training and deployment Book Description: MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization. You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow. By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built. What You Will Learn: Understand the different stages of a machine learning project Use open source software to build a machine learning platform on Kubernetes Implement a complete ML project using the machine learning platform presented in this book Improve on your organization's collaborative journey towa

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