The coverage of model serving is comprehensive. It contrasts different approaches, from simple Flask apps to more complex inference servers like Seldon Core. This provides a good "menu" of options for architects deciding how to serve their models in production.
Automates horizontal and vertical scaling for computing clusters, allowing massive data preparation tasks or parallel deep learning training sessions to scale out and spin down dynamically. faisal masood machine learning on kubernetes
"Machine Learning on Kubernetes" is a necessary evolution in the MLOps literature canon. While many books focus on the algorithms of AI or the syntax of DevOps tools, this book fills the critical gap between the two: . It is a highly practical, blueprint-heavy guide designed for the DevOps engineer transitioning into MLOps, or the Data Engineer tired of fragile Jupyter notebooks. However, it is not for the absolute beginner; it demands a working knowledge of both Python and basic containerization concepts to be truly useful. The coverage of model serving is comprehensive
While it covers NVIDIA device plugins, it doesn’t dive into multi‑node GPU training (e.g., distributed PyTorch with torch.distributed on K8s). That’s a significant omission for teams doing large‑scale training. It is a highly practical, blueprint-heavy guide designed