Name: ModelDB for Machine Learning Workflows: Practical Experiment Tracking and Reproducibility
SKU: 68912660
Price: 41.23 EUR
Availability: InStock
Author: Johnson, Robert U.
ISBN: 9798170897223

portada ModelDB for Machine Learning Workflows: Practical Experiment Tracking and Reproducibility

ModelDB for Machine Learning Workflows: Practical Experiment Tracking and Reproducibility

Type

Physical Book

Publisher

Independently published

Author

Johnson, Robert U.

Format

Paperback

ISBN13

9798170897223

ModelDB for Machine Learning Workflows: Practical Experiment Tracking and Reproducibility

Johnson, Robert U. (Author) · Independently published · Paperback

New Book Imported to Netherlands

Delivery: 13 Oct - 15 Oct

€ 41,23

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€ 41,23

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Synopsis "ModelDB for Machine Learning Workflows: Practical Experiment Tracking and Reproducibility"

“ModelDB for Machine Learning Workflows: Practical Experiment Tracking and Reproducibility” is a comprehensive guide to managing machine learning experiments in a way that is reproducible, collaborative, and scalable. It explains why experiment tracking matters, showing how teams can systematically capture the details behind every run, compare results with confidence, and preserve the context needed to revisit and extend prior work. The book provides a clear foundation in the core ideas, challenges, and design goals of modern experiment management systems. At its core, the book explores ModelDB’s architecture and the metadata model that supports it, including storage design, API structure, graph-based lineage tracking, and security considerations. It walks readers through the practical capture of experiment information such as datasets, code versions, hyperparameters, artifacts, and evaluation metrics, while also discussing the trade-offs involved in building reliable tracking infrastructure. Integration examples demonstrate how ModelDB fits into common machine learning environments, including scikit-learn, TensorFlow, and PyTorch, as well as data versioning tools, CI/CD pipelines, and broader MLOps workflows. Beyond the fundamentals, the book covers analysis, visualization, scalability, and extensibility, offering guidance on using ModelDB effectively in real-world settings. It also presents case studies, best practices, and lessons learned from production and research deployments, highlighting how systematic experiment tracking can improve productivity and trust in machine learning work. Whether for researchers, engineers, or platform architects, this book equips readers with practical tools for building reproducible ML workflows and supporting long-term innovation.