Machine Learning (ML) is a form of AI that lets a system continuously learn from data through virtuous algorithms rather than explicit programming. It offers potential value for companies that use data to better understand the subtle changes in their customers’ behaviours, preferences and levels of satisfaction.
But despite these capabilities, machine learning also comes with challenges and risks. Firstly, complex ML models need to be regularly refreshed, which can incur high production deployment costs. Secondly, if data quality is not closely monitored, the AI can quickly suffer from performance drift and bias.
To solve these challenges, we close the gap between Proofs of Concepts (POC) and Production by applying our Machine Learning Operations (MLOps) methodology to all of our Data and AI projects.
Our methodology is inspired by the DevOps approach used by the most innovative software companies, combining software development (Dev) and IT operations (Ops).
It aims to shorten the systems development life cycle and provide continuous delivery with high software quality.