Machine Learning on Kubernetes

In "Machine Learning on Kubernetes", authors Faisal Masood and None Brigoli provide a comprehensive guide to building a complete, open-source machine learning platform using Kubernetes. Through a practical, hands-on approach, you will learn to set up automation and workflows that streamline the machine learning lifecycle, allowing for rapid experimentation, development, and deployment of intelligent applications.

What this Book will help me do

• Design and implement each step of a machine learning project's lifecycle on Kubernetes.
• Build an open-source, end-to-end machine learning platform using tools like MLflow and Airflow.
• Automate data pipelines and machine learning workflows for faster iteration and deployment.
• Collaborate cross-functionally using an MLOps platform to enhance organizational productivity in machine learning initiatives.
• Develop a deep understanding of Kubernetes and its applications in orchestrating machine learning tasks.

Author(s)

Faisal Masood and None Brigoli are seasoned technologists with extensive experience in machine learning, cloud computing, and enterprise systems. Faisal is particularly recognized for his expertise in architecture design for scalable data systems, while None focuses on marrying machine learning with practical applications. Together, their pragmatic approach ensures this book is rich in real-world usability.

Who is it for?

This book is for data scientists who aim to expand their practical knowledge of MLOps platforms, data engineers who want to automate machine learning pipelines on Kubernetes, and technical platform owners seeking to build collaborative platforms for machine learning. Readers should ideally have experience with Python, Kubernetes, and basic data science concepts to make the most of the material provided.