February 2026
Intermediate to advanced
406 pages
11h 42m
English
Content preview from Generative AI on Kubernetes
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Part III. Tuning
This part covers LLM tuning, focusing on the operational challenges of managing and optimizing these demanding workloads in Kubernetes. A key distinction in the generative AI lifecycle is the shift from training to tuning. Unlike traditional machine learning, which often involves training a model from scratch, here we typically start with a large, pre-trained foundation model. Customization is an optional, subsequent step to specialize that model for a specific purpose. The goal is refinement, not creation from the ground up.
While the techniques for model tuning are rapidly evolving, the operational hurdles of scheduling, resource management, and cost optimization remain constant.
The chapters in this part cover how to address these challenges within the Kubernetes ecosystem:
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Chapter 6, “Model Customization”, describes some of the techniques to customize an LLM, with a focus on the common challenges and how projects in Kubernetes ecosystem solve them.
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Chapter 7, “Job Scheduling Optimization”, focuses on job scheduling, quota management, and tuning the GPU configuration for the tuning workload on Kubernetes.
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