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Everything about MLOps
Approach to machine learning lifecycle management
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. It bridges the gap between machine learning development and production operations, ensuring that models are robust, scalable, and aligned with business goals. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: MLOpsA 5-minute orientation
- Skim a recent paper: Towards a machine learning operations (MLOps) soft sensor for real-time predictions in industrial-scale fed-batch fermentationInfluential work since 2022
- Pick up a classic book: Generative AI on KubernetesThe most-reprinted book on the subject
- Read the landmark paper: Machine Learning Operations (MLOps): Overview, Definition, and ArchitectureCited 750 times
- Go sideways: ModelOpsA closely related topic
- Go sideways: DevOpsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Machine Learning Operations (MLOps): Overview, Definition, and Architecture
MLOps: A Taxonomy and a Methodology
Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
A Joint Study of the Challenges, Opportunities, and Roadmap of MLOps and AIOps: A Systematic Survey
Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models
From DevOps to MLOps: Overview and Application to Electricity Market Forecasting
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
MLOps best practices, challenges and maturity models: A systematic literature review
🆕 Influential since 2022
Towards a machine learning operations (MLOps) soft sensor for real-time predictions in industrial-scale fed-batch fermentation
Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm
A Multivocal Review of MLOps Practices, Challenges and Open Issues
MLOps Challenges in Industry 4.0
Resilience-aware MLOps for AI-based medical diagnostic system
📚 Books worth reading
Generative AI on KubernetesRoland Huß, Daniele Zonca · 2026
DevOps for AI-Native PlatformsNabeel Khan · 2026
LLM Systems in ProductionNabeel Khan · 2026
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- ModelOps ModelOps (model operations or model operationalization), as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide…
- DevOps DevOps is the integration and automation of software development and information technology operations. DevOps encompasses the tasks necessary for software…
- Systems design The basic study of system design is the understanding of component parts and their subsequent interaction with one another. Systems design has appeared…
- AIOps AIOps (Artificial Intelligence for IT Operations) is the use of artificial intelligence, machine learning, and big data analytics to automate and enhance…
- Artificial intelligence engineering Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI…
- Continuous delivery Continuous delivery (CD) is a software engineering approach in which teams produce software in short cycles, ensuring that the software can be reliably…
- Site reliability engineering Site reliability engineering (SRE) is a discipline in the field of software engineering and IT infrastructure support that monitors and improves the availabilit…
- AI observability AI observability is the practice of collecting and analyzing telemetry, data such as logs, metrics, and traces that a system automatically records as it…