MLOps is a term you have probably started hearing increasingly often in the machine learning space — and for good reason. Industry awareness of MLOps is growing, as more and more businesses realize that the hacky, works-on-my-machine workflows that data scientists can use to quickly leverage newly built models internally do not scale to the large datasets, user demands, and business oversight that come with production deployments into enterprise products.
This is critically important for non-technical stakeholders and business leaders, who need to make on-the-fly decisions based on a project’s progress and potential.
In this example, it is the job of business leaders looped into this process to communicate the business goal to the product team, and and it is the job of the product team to transform this into the model metric threshold (cross-entropy loss, hinge loss, etc).
This process will likely involve human-in-the-loop review of model performance metrics for product stakeholders and engineering briefs for business stakeholders that need to rubber-stamp the deployment.