MLOpskäytännöt

MLOpskäytännöt refers to the set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. It is an extension of DevOps principles applied to the machine learning lifecycle. The core idea is to bridge the gap between data scientists who build models and operations teams who deploy and manage them.

Key aspects of MLOpskäytännöt include continuous integration (CI), continuous delivery (CD), and continuous training (CT). CI involves automating the testing and merging of code changes, while CD automates the release of models to production. CT focuses on automatically retraining models when new data becomes available or performance degrades. Version control for both code and data is crucial, enabling reproducibility and tracking of model evolution.

Monitoring is another vital component, ensuring that deployed models perform as expected and detecting drift or anomalies. This includes tracking model accuracy, latency, and resource utilization. Automation of the entire ML lifecycle, from data preparation to model deployment and monitoring, is a central theme. MLOpskäytännöt also emphasizes collaboration between different teams, fostering a shared responsibility for the success of machine learning initiatives. The ultimate goal is to accelerate the deployment of ML models, improve their reliability, and ensure their ongoing value to the business.