Validating and monitoring the model
After you’ve successfully trained and deployed your ML model, the journey doesn’t end there. Model validation and monitoring are the important next steps in your MLOps process. We will briefly discuss validating your deployed model and then focus on monitoring it long-term.
Validating the model deployment
Once your model is deployed, you will want to validate that it works as expected. This is a relatively short and straightforward process. The general steps involve connecting to your deployed model, submitting some data (preferably data unseen by the model during the training process), collecting the model predictions, and scoring them.
This will allow you to confirm a couple of things. First, you know that your deployment worked, and your model is returning results. Secondly, if you submit unseen data to the model and score it, this will give you another assessment of the model’s performance. You don’t want...
