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What is MLOps?

MLOps or ML Ops stands for ‘machine learning operations’ and can be described as DevOpsfor machine learning. MLOps enables data science and IT professionals to collaborate, and accelerate model development and implementation through the monitoring, validation and management of machine learning models. One advantage of MLOps is that it can enable you to deliver innovation faster.

MlOps is thus a set of practices at the interface between machine learning, DevOps and computer engineering. MLOps aims to distribute and maintain machine learning models in production – reliable and efficient.

The word is a combination of machine learning and continuous development of DevOps in the field of software. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready for launch, MLOps is practised between engineers in Data science, DevOps and Machine learning in order to transfer the algorithm to production systems.

Like the DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while at the same time focusing on business and regulatory requirements. MLOps began as a set of best practices and has evolved into an independent approach to ML lifecycle management.

MLOps covers the entire lifecycle — from integration with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration and distribution, to health, diagnostics, management and business metrics.

According to Gartner, MLOps is a subset of ModelOps. MLOps focuses on the operationalisation of ML models, while ModelOps covers the operationalisation of all types of AI models.

Cegal and MLOps

At Cegal, we value working in teams, and we invest resources in MLOps. MLOps helps us to put machine learning models into production, through provision for facilitation between data science teams and operations teams.

By their very nature, models are not static, and we therefore use MLOps to help our teams quickly adjust or alter our models during production. This faster management intervention is especially important as regards the prevention of unfair treatment, which is important for us here at Cegal.

MLOps increases the credibility, reliability and productivity of our machine learning development, giving our customers great business value.

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