MLOps (short for machine learning operations) is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring. It will become a standard as artificial intelligence is moving towards becoming part of everyday business, rather than an innovative activity.
Over time, there have been different approaches used in MLOps. The most popular ones are model-driven and data-driven approaches. The split between them is defined by the main focus of the AI system: data or code. Which one should you choose? The decision challenges data scientists to choose which component will play a more important role in the development of a robust model. In this blog, we will evaluate both.
Model-centric development
Model-driven development focuses, as the name suggests, on machine learning model performance. It uses…
