Machine learning on Kubernetes :: a practical handbook for building and using a complete open source machine learning platform on Kubernetes /
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-s...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing,
2022.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way. |
| Beschreibung: | 1 online resource |
| ISBN: | 1803231653 9781803231655 |
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| 520 | |a 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 toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way. | ||
| 505 | 0 | |a Table of Contents Challenges in Machine Learning Understanding MLOps Exploring Kubernetes The Anatomy of a Machine Learning Platform Data Engineering Machine Learning Engineering Model Deployment and Automation Building a Complete ML Project Using the Platform Building Your Data Pipeline Building, Deploying and Monitoring Your Model Machine Learning on Kubernetes. | |
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| contents | Table of Contents Challenges in Machine Learning Understanding MLOps Exploring Kubernetes The Anatomy of a Machine Learning Platform Data Engineering Machine Learning Engineering Model Deployment and Automation Building a Complete ML Project Using the Platform Building Your Data Pipeline Building, Deploying and Monitoring Your Model Machine Learning on Kubernetes. |
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| spelling | Masood, Faisal, author. Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / Faisal Masood, Ross Brigoli. Birmingham : Packt Publishing, 2022. 1 online resource text rdacontent computer rdamedia online resource rdacarrier Description based on CIP data; resource not viewed. 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 toward machine learning Discover how to use the platform as a data engineer, ML engineer, or data scientist Find out how to apply machine learning to solve real business problems Who this book is for This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way. Table of Contents Challenges in Machine Learning Understanding MLOps Exploring Kubernetes The Anatomy of a Machine Learning Platform Data Engineering Machine Learning Engineering Model Deployment and Automation Building a Complete ML Project Using the Platform Building Your Data Pipeline Building, Deploying and Monitoring Your Model Machine Learning on Kubernetes. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Open source software. http://id.loc.gov/authorities/subjects/sh99003437 Apprentissage automatique. Logiciels libres. Machine learning fast Open source software fast Brigoli, Ross, author. Print version: 9781803241807 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3291290 Volltext |
| spellingShingle | Masood, Faisal Brigoli, Ross Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / Table of Contents Challenges in Machine Learning Understanding MLOps Exploring Kubernetes The Anatomy of a Machine Learning Platform Data Engineering Machine Learning Engineering Model Deployment and Automation Building a Complete ML Project Using the Platform Building Your Data Pipeline Building, Deploying and Monitoring Your Model Machine Learning on Kubernetes. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Open source software. http://id.loc.gov/authorities/subjects/sh99003437 Apprentissage automatique. Logiciels libres. Machine learning fast Open source software fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh99003437 |
| title | Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / |
| title_auth | Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / |
| title_exact_search | Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / |
| title_full | Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / Faisal Masood, Ross Brigoli. |
| title_fullStr | Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / Faisal Masood, Ross Brigoli. |
| title_full_unstemmed | Machine learning on Kubernetes : a practical handbook for building and using a complete open source machine learning platform on Kubernetes / Faisal Masood, Ross Brigoli. |
| title_short | Machine learning on Kubernetes : |
| title_sort | machine learning on kubernetes a practical handbook for building and using a complete open source machine learning platform on kubernetes |
| title_sub | a practical handbook for building and using a complete open source machine learning platform on Kubernetes / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Open source software. http://id.loc.gov/authorities/subjects/sh99003437 Apprentissage automatique. Logiciels libres. Machine learning fast Open source software fast |
| topic_facet | Machine learning. Open source software. Apprentissage automatique. Logiciels libres. Machine learning Open source software |
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