MLRun Feature Store in Practice (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"MLRun Feature Store in Practice" "MLRun Feature Store in Practice" is a comprehensive guide for data scientists, MLOps practitioners, and enterprise architects seeking to master feature store concepts, engineering workflows, and production deployment using MLRun. The book opens with a thorough exploration of feature stores as a cornerstone of modern machine learning pipelines, delving into their evolution, architectural foundations, and the unique advantages offered by MLRun over competing solutions. Readers are introduced to best practices in security, scalability, and deployment, providing ...
"MLRun Feature Store in Practice"
"MLRun Feature Store in Practice" is a comprehensive guide for data scientists, MLOps practitioners, and enterprise architects seeking to master feature store concepts, engineering workflows, and production deployment using MLRun. The book opens with a thorough exploration of feature stores as a cornerstone of modern machine learning pipelines, delving into their evolution, architectural foundations, and the unique advantages offered by MLRun over competing solutions. Readers are introduced to best practices in security, scalability, and deployment, providing a roadmap for aligning MLOps strategies with regulatory and business imperatives.
Bridging theory and real-world application, this book covers every aspect of feature engineering in distributed, real-time, and batch environments. Topics include advanced data ingestion, pipeline orchestration, point-in-time correctness, automated validation, and collaborative versioning of features. The text guides readers through the lifecycle of features, from schema design and lineage tracking to cataloging, governance, and multi-tenancy, ensuring both control and agility in large-scale, collaborative data science initiatives.
Finally, "MLRun Feature Store in Practice" takes readers deep into the operational backbone that supports resilient, high-performance ML workflows. It details robust pipeline patterns, online and offline store architectures, end-to-end monitoring, and techniques for integrating feature stores with model pipelines. Specialized chapters address advanced use cases-ranging from real-time personalization to IoT, fraud detection, and business process automation-while practical guidance on extension, customization, performance tuning, and disaster recovery empowers organizations to transform their ML operations with confidence and efficiency.
"MLRun Feature Store in Practice" is a comprehensive guide for data scientists, MLOps practitioners, and enterprise architects seeking to master feature store concepts, engineering workflows, and production deployment using MLRun. The book opens with a thorough exploration of feature stores as a cornerstone of modern machine learning pipelines, delving into their evolution, architectural foundations, and the unique advantages offered by MLRun over competing solutions. Readers are introduced to best practices in security, scalability, and deployment, providing a roadmap for aligning MLOps strategies with regulatory and business imperatives.
Bridging theory and real-world application, this book covers every aspect of feature engineering in distributed, real-time, and batch environments. Topics include advanced data ingestion, pipeline orchestration, point-in-time correctness, automated validation, and collaborative versioning of features. The text guides readers through the lifecycle of features, from schema design and lineage tracking to cataloging, governance, and multi-tenancy, ensuring both control and agility in large-scale, collaborative data science initiatives.
Finally, "MLRun Feature Store in Practice" takes readers deep into the operational backbone that supports resilient, high-performance ML workflows. It details robust pipeline patterns, online and offline store architectures, end-to-end monitoring, and techniques for integrating feature stores with model pipelines. Specialized chapters address advanced use cases-ranging from real-time personalization to IoT, fraud detection, and business process automation-while practical guidance on extension, customization, performance tuning, and disaster recovery empowers organizations to transform their ML operations with confidence and efficiency.
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