Machine learning upgrade
a data scientists guide to MLOps LLMs, and ML Infrastructure
Bibliographic details
Additional authors
Kaiser, Caleb
Summary
This book provides a practical framework for building scalable and production-ready machine learning and LLM applications. It covers MLOps practices, data versioning, prompt engineering, fine-tuning, monitoring, deployment, vector databases, and ML infrastructure using modern AI workflows.