Fundamentals
The core MLOps guidelines for production ML
In this article, we will look at the core MLOps guidelines, independent of any tool, to design robust and scalable production ML systems and architectures:
- Automation or operationalization
- Versioning
- Experiment tracking
- Testing
- Monitoring
- Reproducibility
Let’s start by looking into the foundations of automation (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…