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What is ML-Ops? Why is it important? (MLOps-1)
Businesses around the globe use machine learning to predict their sales, profits, costs, performance, and use these predictions to build actionable insights. These insights help decision-makers to take the right decision according to the current situation after learning from historical experiences.
While ML-teams focus on modelling and SW teams focus on development, I could rarely find a team focusing on ML operations or MLOps for short. in this article, we will explain what is MLOps and why it is important.
What is MLOps?
MLOps has two interpretations, ML-Development Operations and ML-Runtime Operations. ML-DevOps focuses on taking the model from coding to production enabling the CI/CD feature (Continuous Integration/Continuous Deployment). This feature will take the code through all the stages of source control, virtualization, building, deployment, hosting and scaling.
ML-RuntimeOps focus on maintaining the model after deployment, to avoid problems such as data drift, concept drift, anomalies, discriminatory bias and underperforming segment. ML-RuntimeOps aims to detect the problems early and fix them before they happen. This basically requires enabling Continuous Evaluation, Continuous Training and Continuous Deployment of the model at the runtime. And in the worst-case scenario, it will require enabling the model-challenger to avoid service disruption.
Why MLOps Should be Automated?
MLOps require lots of human interference along every step of the road to deployment and after-deployment maintenance as well. This human interference costs a lot of effort, time and money that can be minimized via automation. Various statistical studies concluded that the manual MLOps has the following…
