MLRun Orchestration for Machine Learning Operations (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"MLRun Orchestration for Machine Learning Operations" "MLRun Orchestration for Machine Learning Operations" is an in-depth guide to mastering modern MLOps through the lens of MLRun, an innovative orchestration platform designed to bring scalability, flexibility, and efficiency to machine learning workflows. The book begins by positioning MLRun in the rapidly evolving MLOps landscape, offering historical context, foundational design principles, and a rich comparative analysis against other orchestrators like Kubeflow, Airflow, and Argo. Readers gain a thorough understanding of where MLRun fits ...
"MLRun Orchestration for Machine Learning Operations"
"MLRun Orchestration for Machine Learning Operations" is an in-depth guide to mastering modern MLOps through the lens of MLRun, an innovative orchestration platform designed to bring scalability, flexibility, and efficiency to machine learning workflows. The book begins by positioning MLRun in the rapidly evolving MLOps landscape, offering historical context, foundational design principles, and a rich comparative analysis against other orchestrators like Kubeflow, Airflow, and Argo. Readers gain a thorough understanding of where MLRun fits within the end-to-end machine learning lifecycle, its integration points, deployment architectures, and the key abstractions that underpin its extensibility and modularity.
Delving deeper, the book explores the architectural underpinnings of MLRun, including its robust orchestration engine, tight Kubernetes integration, advanced data management capabilities, and secure, governed operation at scale. Practical chapters equip readers to design and implement resilient, idempotent ML pipelines-ranging from ETL and real-time data streaming to experiment management, hyperparameter tuning, and distributed training-while ensuring reproducibility, lineage, and seamless integration with leading ML frameworks. Dedicated sections address the complexities of model deployment, serving, scaling, and monitoring in multi-tenant, hybrid, and multi-cloud environments, underscored by automated recovery, drift detection, and compliance best practices.
The final chapters empower organizations to embrace continuous delivery, CI/CD, and automation in their ML operations with GitOps-driven workflows, automated testing, and environment management. With actionable insights on scaling MLRun to enterprise deployments, optimizing resources and costs, implementing advanced security, and future-proofing workflows for emerging paradigms such as federated learning and edge AI, this book is an indispensable resource for engineers, architects, and data science leaders seeking to operationalize machine learning with rigor, agility, and confidence.
"MLRun Orchestration for Machine Learning Operations" is an in-depth guide to mastering modern MLOps through the lens of MLRun, an innovative orchestration platform designed to bring scalability, flexibility, and efficiency to machine learning workflows. The book begins by positioning MLRun in the rapidly evolving MLOps landscape, offering historical context, foundational design principles, and a rich comparative analysis against other orchestrators like Kubeflow, Airflow, and Argo. Readers gain a thorough understanding of where MLRun fits within the end-to-end machine learning lifecycle, its integration points, deployment architectures, and the key abstractions that underpin its extensibility and modularity.
Delving deeper, the book explores the architectural underpinnings of MLRun, including its robust orchestration engine, tight Kubernetes integration, advanced data management capabilities, and secure, governed operation at scale. Practical chapters equip readers to design and implement resilient, idempotent ML pipelines-ranging from ETL and real-time data streaming to experiment management, hyperparameter tuning, and distributed training-while ensuring reproducibility, lineage, and seamless integration with leading ML frameworks. Dedicated sections address the complexities of model deployment, serving, scaling, and monitoring in multi-tenant, hybrid, and multi-cloud environments, underscored by automated recovery, drift detection, and compliance best practices.
The final chapters empower organizations to embrace continuous delivery, CI/CD, and automation in their ML operations with GitOps-driven workflows, automated testing, and environment management. With actionable insights on scaling MLRun to enterprise deployments, optimizing resources and costs, implementing advanced security, and future-proofing workflows for emerging paradigms such as federated learning and edge AI, this book is an indispensable resource for engineers, architects, and data science leaders seeking to operationalize machine learning with rigor, agility, and confidence.
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