Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Faisal Masood, Ross Brigoli

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.

MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations

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.

The Kubernetes Workshop. Learn how to build and run highly scalable workloads on Kubernetes

Zachary Arnold, Sahil Dua, Wei Huang, Faisal Masood, ...

Thanks to its extensive support for managing hundreds of containers that run cloud-native applications, Kubernetes is the most popular open source container orchestration platform that makes cluster management easy. This workshop adopts a practical approach to get you acquainted with the Kubernetes environment and its applications.Starting with an introduction to the fundamentals of Kubernetes, you’ll install and set up your Kubernetes environment. You’ll understand how to write YAML files and deploy your first simple web application container using Pod. You’ll then assign human-friendly names to Pods, explore various Kubernetes entities and functions, and discover when to use them. As you work through the chapters, this Kubernetes book will show you how you can make full-scale use of Kubernetes by applying a variety of techniques for designing components and deploying clusters. You’ll also get to grips with security policies for limiting access to certain functions inside the cluster. Toward the end of the book, you’ll get a rundown of Kubernetes advanced features for building your own controller and upgrading to a Kubernetes cluster without downtime.By the end of this workshop, you’ll be able to manage containers and run cloud-based applications efficiently using Kubernetes.