Understanding ModelOps

ModelOps is a set of practices for automating a common set of operations that arise in data science projects, which include the following:

• Model training pipeline
• Data management
• Version control
• Experiment tracking
• Testing
• Deployment

Without ModelOps, teams are forced to waste time on those repetitive tasks. Each task in itself is fairly easy to handle, but a project can suffer from mistakes in those steps. ModelOps helps us to create project delivery pipelines that work like a precise conveyor belt with automated testing procedures that try to catch coding errors.

Let's start by discussing ModelOps' closest cousin—DevOps.