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 DescriptionMLOps 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 forThis 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.

From building your own cluster to running cloud-native applications with Kubernetes, this workshop covers it all using engaging examples and activities Key Features Explore the Kubernetes environment and understand how containers are managed Learn how to build, maintain, and deploy cloud-native applications using Kubernetes Get to grips with using Kubernetes primitives to manage the life cycle of a full application stack Book DescriptionThanks 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. What you will learn Get to grips with the fundamentals of Kubernetes and its terminology Share or store data in different containers running in the same pod Create a container image from an image definition manifest Construct a Kubernetes-aware continuous integration (CI) pipeline for deployments Attract traffic to your app using Kubernetes ingress Build and deploy your own admission controller Who this book is forWhether you are new to the world of web programming or are an experienced developer or software engineer looking to use Kubernetes for managing and scaling containerized applications, you'll find this workshop useful. A basic understanding of Docker and containerization is necessary to make the most of this book.