Tutorial: Learning MLOps with Vector - Part 1
MLOps is what enables enterprises to deliver Machine Learning in production
Figure 1: The complete lifecycle of ML Operations (MLOps)
In a previous tutorial, we learnt how to train the Anki Vector robot to recognize another Vector robot. Specifically, we learnt how to train a YOLOv5 model to detect the Vector robot in a picture taken with Vector’s camera. We leveraged the publicly available Vector dataset to train the model.
While that was an interesting model training exercise, the process was not mature enough for a production deployment. Meaning that, if Digital Dream Labs (DDL) (the current owner of the Vector robot) were to use this model to deliver a production quality feature that enables all Vector robots to recognize other Vector robots, they would have to do a lot more than merely train a ML model. They would likely have to build an entire automated end-to-end pipeline which consists of: (i)Collecting new data (ii)Labeling them, (iii)Training new models, (iv)Deciding if the newly trained models should be deployed, (v)Rolling back models if required, a…