MLOps with Red Hat OpenShift : a cloud-native approach to machine learning operations.
Author
Published
Birmingham, UK : Packt Publishing, Limited, 2024.
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Available Online
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Language
English
ISBN
1805125850, 9781805125853
Notes
General Note
Description based upon print version of record.
Bibliography
Includes bibliographical references and index.
Description
Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book Description MLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you'll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you'll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you'll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you'll be able to implement MLOps workflows on the OpenShift platform proficiently. What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you're a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.
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O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Brigoli, R., & Masood, F. (2024). MLOps with Red Hat OpenShift: a cloud-native approach to machine learning operations . Packt Publishing, Limited.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Brigoli, Ross and Faisal, Masood. 2024. MLOps With Red Hat OpenShift: A Cloud-native Approach to Machine Learning Operations. Birmingham, UK: Packt Publishing, Limited.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Brigoli, Ross and Faisal, Masood. MLOps With Red Hat OpenShift: A Cloud-native Approach to Machine Learning Operations Birmingham, UK: Packt Publishing, Limited, 2024.
Harvard Citation (style guide)Brigoli, R. and Masood, F. (2024). Mlops with red hat openshift: a cloud-native approach to machine learning operations. Birmingham, UK: Packt Publishing, Limited.
MLA Citation, 9th Edition (style guide)Brigoli, Ross,, and Faisal Masood. MLOps With Red Hat OpenShift: A Cloud-native Approach to Machine Learning Operations Packt Publishing, Limited, 2024.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
7842f33f-015f-e95d-fcf8-5cf0bbb1a9cb-eng
Grouping Information
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| Full title | mlops with red hat openshift a cloud native approach to machine learning operations |
| Author | brigoli ross |
| Grouping Category | book |
| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-05-22 03:23:19AM |
Book Cover Information
| Image Source | default |
|---|---|
| First Loaded | Jan 18, 2025 |
| Last Used | Jan 18, 2025 |
Marc Record
| First Detected | Dec 16, 2024 11:29:53 PM |
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| Last File Modification Time | Dec 17, 2024 08:29:04 AM |
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: Introduction -- Chapter 1: Introduction to MLOps and OpenShift -- What is MLOps? -- Introduction to OpenShift -- OpenShift features -- Understanding operators -- Understanding how OpenShift supports MLOps -- Red Hat OpenShift Data Science (RHODS) -- The advantages of the cloud -- ROSA -- Summary -- References -- Part 2: Provisioning and Configuration -- Chapter 2: Provisioning an MLOps Platform in the Cloud -- Technical requirements -- Installing OpenShift on AWS | |
| 505 | 8 | |a Preparing AWS accounts and service quotas -- Preparing AWS for ROSA provisioning -- Installing ROSA -- Adding a new machine pool to the cluster -- Installing Red Hat ODS -- Installing partner software on RedHat ODS -- Installing Pachyderm -- Summary -- Chapter 3: Building Machine Learning Models with OpenShift -- Technical requirements -- Using Jupyter Notebooks in OpenShift -- Provisioning an S3 store -- Using ML frameworks in OpenShift -- Using GPU acceleration for model training -- Enabling GPU support -- Building custom notebooks -- Creating a custom notebook image | |
| 505 | 8 | |a Importing notebook images -- Summary -- Part 3: Operating ML Workloads -- Chapter 4: Managing a Model Training Workflow -- Technical requirements -- Configuring Pachyderm -- Versioning your data with Pachyderm -- Training a model using Red Hat ODS -- Building a model training pipeline -- Installing Red Hat OpenShift Pipelines -- Attaching a pipeline server to your project -- Building a basic data science pipeline -- Summary -- Chapter 5: Deploying ML Models as a Service -- Packaging and deploying models as a service -- Saving and uploading models to S3 | |
| 505 | 8 | |a Updating the pipeline via model upload to S3 -- Creating a model server for Seldon -- Deploying and accessing your model -- Autoscaling the deployed models -- Releasing new versions of the model -- Automating the model deployment process -- Rolling back model deployments -- Canary model deployment -- Securing model endpoints -- Summary -- Chapter 6: Operating ML Workloads -- Monitoring ML models -- Installing and configuring Prometheus and Grafana -- Logging inference calls -- Optimizing cost -- Summary -- References -- Chapter 7: Building a Face Detector Using the Red Hat ML Platform | |
| 505 | 8 | |a Architecting a human face detector system -- Training a model for face detection -- Deploying the model -- Validating the deployed model -- Installing Redis on Red Hat OpenShift -- Building and deploying the inferencing application -- Bringing it all together -- Optimizing cost for your ML platform -- Machine management in OpenShift -- Spot Instances -- Summary -- Index -- Other Books You May Enjoy | |
| 520 | |a Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book Description MLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you'll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you'll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you'll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you'll be able to implement MLOps workflows on the OpenShift platform proficiently. What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you're a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform. | ||
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