Efficient MLOps is a detailed strategy for streamlining machine-learning operations through GitHub-centric workflows and automation. From setting up efficient training pipelines to implementing real-time model monitoring—this book presents battle-tested best practices for ML Engineers.Learn the art of continuous integration and deployment for machine-learning projects using GitHub Actions and modern MLOps tools. Learn practical techniques for data versioning, model serving, and maintaining high-performance ML systems in production. Transform your ML projects from experimental notebooks to production-ready systems with industry-standard practices and workflows.Whether you're a Data Scientist or ML Engineer, this book provides the knowledge to build and maintain scalable ML systems using GitHub's innovative ecosystem.
Seol RohKi-Moon JeongHyeyoung ChoEui‐Nam Huh
Eduardo PeixotoDouglas TorresDavide CarneiroBruno SilvaPaulo Nováis
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