MLOps is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering[1]
MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently.[1] The word is a compound of "machine learning" and the continuous development practice of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.[2] Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. According to Gartner, MLOps is a subset of ModelOps. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models.[3]
^ abBreuel, Cristiano. "ML Ops: Machine Learning as an Engineering Discipline". Towards Data Science. Retrieved 6 July 2021.
^Talagala, Nisha. "Why MLOps (and not just ML) is your Business' New Competitive Frontier". AITrends. Retrieved 30 January 2018.
requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the...
plug-ins, and most importantly, the business and compliance/risk KPI's. MLOps (machine learning operations) is a discipline that enables data scientists...
Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google. The different stages in a typical machine learning...
million to back new startups". VentureBeat. Retrieved February 19, 2018. "MLOps platform Landing AI raises $57M to help manufacturers adopt computer vision"...
choice for deploying web applications and APIs. The service also simplifies MLOps. AWS App Runner offers several features that are designed to simplify the...
multi-national companies, contributions to spatial technologies, and as a pioneer of MLOps as an AI methodology. Wise demonstrated the ability of his AI Platform's...
Python Operating system Linux, Mac OS X, Microsoft Windows Type Automation, MLOps, DevOps, Package management system, Workflow management system, Software...
including administrative work, diagnostics, nursing and counseling. The MLOPs are primarily women who are high-school graduates recruited from surrounding...
idea". The Register. Retrieved 14 October 2019. ""Interviews from the 2019 MLOps Conference"". Inside Analysis. Retrieved 14 December 2020. Alex, Woodie...
Retrieved August 22, 2023. "Deepset secures $30M to expand its LLM-focused MLOps offerings". TechCrunch. 9 August 2023. Retrieved August 22, 2023. "Deepset...
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distribution, validation, and deployment. It is very similar to the later MLOps concepts. Recently this topic attracts attention in top system conferences...
to analyze and improve player performance); Neptune (Metadata store for MLOps, built for research and production teams that run a lot of experiments);...
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