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MLOps is the set of practices at the intersection of Machine Learning, DevOps and Data Engineering[1]

MLOps or ML Ops is a set of practices 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]


The challenges of the ongoing use of machine learning in applications were highlighted in a 2015 paper.[4]

The predicted growth in machine learning included an estimated doubling of ML pilots and implementations from 2017 to 2018, and again from 2018 to 2020.[5]

Reports show a majority (up to 88%) of corporate AI initiatives are struggling to move beyond test stages[citation needed]. However, those organizations that actually put AI and machine learning into production saw a 3-15% profit margin increases.[6]

The MLOps market was estimated at $23.2billion in 2019 and is projected to reach $126 billion by 2025 due to rapid adoption.[7]


Machine Learning systems can be categorized in eight different categories: data collection, data processing, feature engineering, data labeling, model design, model training and optimization, endpoint deployment, and endpoint monitoring. Each step in the machine learning lifecycle is built in its own system, but requires interconnection. These are the minimum systems that enterprises need to scale machine learning within their organization.


There are a number of goals enterprises want to achieve through MLOps systems successfully implementing ML across the enterprise, including:[8]

  • Deployment and automation[9]
  • Reproducibility of models and predictions[10]
  • Diagnostics[10]
  • Governance and regulatory compliance[11]
  • Scalability[12]
  • Collaboration[13]
  • Business uses[14]
  • Monitoring and management[15]

A standard practice, such as MLOps, takes into account each of the aforementioned areas, which can help enterprises optimize workflows and avoid issues during implementation.

A common architecture of an MLOps system would include data science platforms where models are constructed and the analytical engines where computations are performed, with the MLOps tool orchestrating the movement of machine learning models, data and outcomes between the systems.[8]

See also[edit]

  • ModelOps, 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]
  • AIOps, a similarly named, but different concept - using AI (ML) in IT and Operations.


  1. ^ a b Breuel, Cristiano. "ML Ops: Machine Learning as an Engineering Discipline". Towards Data Science. Retrieved 6 July 2021.
  2. ^ Talagala, Nisha. "Why MLOps (and not just ML) is your Business' New Competitive Frontier". AITrends. AITrends. Retrieved 30 January 2018.
  3. ^ a b Vashisth, Shubhangi; Brethenoux, Erick; Choudhary, Farhan; Hare, Jim. "Use Gartner's 3-Stage MLOps Framework to Successfully Operationalize Machine Learning Projects". Gartner. Gartner. Retrieved 30 October 2020.
  4. ^ Sculley, D.; Holt, Gary; Golovin, Daniel; Davydov, Eugene; Phillips, Todd; Ebner, Dietmar; Chaudhary, Vinay; Young, Michael; Crespo, Jean-Francois; Dennison, Dan (7 December 2015). "Hidden Technical Debt in Machine Learning Systems" (PDF). NIPS Proceedings (2015). Retrieved 14 November 2017.
  5. ^ Sallomi, Paul; Lee, Paul. "Deloitte Technology, Media and Telecommunications Predictions 2018" (PDF). Deloitte. Deloitte. Retrieved 13 October 2017.
  6. ^ Bughin, Jacques; Hazan, Eric; Ramaswamy, Sree; Chui, Michael; Allas, Tera; Dahlström, Peter; Henke, Nicolaus; Trench, Monica. "Artificial Intelligence The Next Digital Frontier?". McKinsey. McKinsey Global Institute. Retrieved 1 June 2017.
  7. ^ "2021 MLOps Platforms Vendor Analysis Report". Retrieved 10 August 2021.
  8. ^ a b Walsh, Nick. "The Rise of Quant-Oriented Devs & The Need for Standardized MLOps". Slides. Nick Walsh. Retrieved 1 January 2018.
  9. ^ "Code to production-ready machine learning in 4 steps". DAGsHub Blog. 2021-02-03. Retrieved 2021-02-19.
  10. ^ a b Warden, Pete. "The Machine Learning Reproducibility Crisis". Pete Warden's Blog. Pete Warden. Retrieved 19 March 2018.
  11. ^ Vaughan, Jack. "Machine learning algorithms meet data governance". SearchDataManagement. TechTarget. Retrieved 1 September 2017.
  12. ^ Lorica, Ben. "How to train and deploy deep learning at scale". O'Reilly. O'Reilly. Retrieved 15 March 2018.
  13. ^ Garda, Natalie. "IoT and Machine Learning: Why Collaboration is Key". IoT Tech Expo. Encore Media Group. Retrieved 12 October 2017.
  14. ^ Manyika, James. "What's now and next in analytics, AI, and automation". McKinsey. McKinsey Global Institute. Retrieved 1 May 2017.
  15. ^ Haviv, Yaron. "MLOps Challenges, Solutions and Future Trends". Iguazio. Iguazio. Retrieved 19 February 2020.