The Most Basic Solution for Building Continuous ML of Pipelines

I think that data science, as I said, is a complex topic and then you tend to over engineer solutions. This is maybe something to bear in mind when you're trying to deploy your first model with a continuous ML stack. The most simple training serving pipeline that I managed to see is something that we actually built within super wise. So basically what are we doing? What is our product? We're observing machine learning models in production. Now in order to do that very well, we need to understand use cases. And it was the first time that we showed that in less than two hours, you can come up with a simple continuousML training serving pipeline.