Rise of MLOps: Machine Learning at Scale

Lithuanian Actuarial Society invites you to the seminar held by Aurimas Griciūnas, Freelancer and Senior MLOps Engineer.

Topic: Rise of MLOps: Machine Learning at Scale
Who: Aurimas Griciūnas (Senior MLOps Engineer at Transactie Monitoring Nederland (TMNL))
When: 2022-08-24, 17:00-19:00
Where: Teams
CPD: 2 formal hours
Price: 50 Eur (LT, LV, EE, FI and Lapland actuarial societies attend for free)
Registration: until 08-22


MLOps or ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. 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. 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.

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