MLOps was inspired by DevOps, and the two approaches are inherently similar. However, there are a few ways in which MLOps differs significantly from DevOps:
MLOps is experimental in nature - most of the activity of data science teams relates to experimentation. Teams constantly change features of their models to achieve better performance, while also managing an evolving codebase.
Hybrid teams - data science teams include both developers (machine learning engineers) and data scientists or researchers who analyze data and develop models and algorithms. The latter might not be experienced at software development.
Continuous testing (CT) - in addition to the regular testing stages of a DevOps pipeline, such as unit tests, functional tests and integration tests, an MLOps pipeline must also continually test the model itself - training it and validating its performance against a known dataset.
Automatic retraining - in most cases, a pre-trained model cannot be used as-is in production. The model needs to be retrained and deployed on an ongoing basis. This requires automating the process data scientists go through to train and validate their models.
Performance degradation - unlike regular software systems, even if a model is working perfectly, performance can degrade over time. This can happen due to unexpected characteristics of data consumed by the model, differences between training and inference pipelines, and unknown biases which can grow with each feedback loop.
Data monitoring - it is not sufficient only to monitor a model as a software system. MLOps teams also need to monitor the data and predictions, to see when the model needs to be refreshed or rolled back.