MLOps levels

MLOps level

MLOps level is a measure of an organization’s maturity in adopting and implementing MLOps practices.

There are a number of different models for assessing MLOps level, but they typically consider factors such as:

• The degree to which CI/CD is used to automate the ML model development and deployment process.
• The use of model versioning and management tools.
• The use of model monitoring and observability tools.
• The level of collaboration between ML development and operations teams.

Some organizations also consider factors such as the use of infrastructure as code (IaC) and the use of cloud-based platforms.

Different levels of MLOps

MLOps level 0: Manual process

Many teams have data scientists and ML researchers who can build state-of-the-art models, but their process for building and deploying ML models is entirely manual. This is considered the basic level of maturity, or level 0.

The following diagram shows the workflow of this process.

Figure: Manual ML steps to serve the model as a prediction service

MLOps level 1: ML pipeline automation