sinanuozdemir/oreilly-hands-on-gpt-llm

Mastering the Art of Scalable and Efficient AI Model Deployment

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This repository helps software engineers, data scientists, and machine learning professionals effectively deploy AI models into production. It provides practical code examples covering everything from selecting third-party LLM providers and cleaning data to advanced deployment techniques like model quantization and Kubernetes integration. The outcome is the ability to build, manage, and optimize AI applications that run reliably and efficiently at scale.

142 stars.

Use this if you are a software engineer or data scientist responsible for getting large language models and other AI applications out of development and into a live, operational environment.

Not ideal if you are looking for an introduction to the theoretical foundations of AI or machine learning algorithms without a focus on practical deployment challenges.

AI deployment MLOps production AI LLM engineering model optimization
No License No Package No Dependents
Maintenance 10 / 25
Adoption 10 / 25
Maturity 8 / 25
Community 24 / 25

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142

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98

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Jupyter Notebook

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Last pushed

Feb 25, 2026

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