Practitioners guide to MLOps

Livro digital

Título:
Practitioners guide to MLOps

Autor:
Khalid Samala, Jarek Kazmierczak, Donna Schut

Categoria:
Tecnologia > IA

Doador:
Raffaello D. N.

Sinopse:
MLOps becomes urgent the moment experiments have to survive real-world delivery, and this white paper makes that challenge concrete through its seven-part lifecycle: ML development, training operationalization, continuous training, model deployment, prediction serving, continuous monitoring, and data and model management. It frames the problem from the start as one of turning machine learning into a repeatable production practice, not just a research workflow. The document walks through the core capabilities that support that lifecycle, including experimentation, data processing, model training and evaluation, online experimentation, model monitoring, ML pipelines, model registry, dataset and feature repositories, and metadata tracking. Its deeper sections connect the operational pieces to governance, traceability, reproducibility, and the handoff between data science and engineering teams. The table of contents makes clear that the paper is structured as a practical framework, moving from overview to detailed process guidance and then to putting the pieces together. Readers get a concise but substantial map for building reliable ML systems at scale, especially in organizations that already know basic machine learning and CI/CD. The value here is the emphasis on disciplined delivery, continuous adaptation, and the operational differences that make ML harder than ordinary software. It is a pragmatic guide for teams that need to ship models safely, monitor them well, and keep them aligned with business goals.