Machine Learning on Kubernetes

Build a Kubernetes-based self-serving agile data science and machine learning ecosystem for your organization using reliable and secure open source technologiesKey FeaturesBuild a complete machine learning platform on KubernetesImprove the agility and velocity of your team by adopting the self-service capabilities of the platformReduce time-to-market by automating data pipelines and model training and deploymentBook 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.Youll begin by understanding the different components of a machine learning project. Then youll design and build a practical end-to-end machine learning project using open source software. As you progress youll 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 youll have learned how to effectively build train and deploy a machine learning model using the machine learning platform you built.What you will learnUnderstand the different stages of a machine learning projectUse open source software to build a machine learning platform on KubernetesImplement a complete ML project using the machine learning platform presented in this bookImprove on your organizations collaborative journey toward machine learningDiscover how to use the platform as a data engineer ML engineer or data scientistFind out how to apply machine learning to solve real business problemsWho 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.Table of ContentsChallenges in Machine LearningUnderstanding MLOpsExploring KubernetesThe Anatomy of a Machine Learning PlatformData EngineeringMachine Learning EngineeringModel Deployment and AutomationBuilding a Complete ML Project Using the PlatformBuilding Your Data PipelineBuilding Deploying and Monitoring Your ModelMachine Learning on Kubernetes