Re Alvarez Parmar

Real-World ML Systems on Kubernetes lays out a comprehensive blueprint for you to follow to deliver model training and deployment at scale with Kubernetes. The book is full of production-grade code samples and hands-on examples that are laser focused on utilizing Kubernetes for data science. You’ll see how Kubernetes can simplify data engineering workflows, and be utilized for advanced machine learning tasks like hyper-parameter optimization, distributed training, and reliably serving massive models. 

Throughout, practical projects keep you grounded in real-world applications, including developing a developer platform for data scientists with JupyterHub, running pipelines with Apache Airflow, and deploying models at scale with Ray.