1. Automation or operationalization
To adopt MLOps, there are three core tiers that most applications build up gradually, from manual processing to full automation:
- Manual process: The process is experimental and iterative in the early stages of developing an ML application. The data scientist manually performs each pipeline step, such as data preparation and validation, model training, and testing. At this point, they commonly use Jupyter notebooks to train their models. This stage’s output is the code used to prepare the data and train the models.
- Continuous training (CT): The next level involves automating model training. This is known as continuous training, which triggers model retraining whenever required. At this point, you often automate your data and model validation steps. This step is usually done by an orchestration tool, such as ZenML, that glues all your code together and runs it on specific triggers. The most common triggers are on a schedule...
