The Exploratory Phase of MLflow

There's this exploratory phase where you try to find potential issues that you wouldn't know about unless you had actually built them in production. I mean, if I personally didn't know either of those, I would be in trouble being a member of the team that maintains MLflow. There's libraries that wrap around transformers or that use transformers like set fit, for instance. That is an abstraction layer around transformers trainer, and it's meant for a specific domain of large language models. And it's a super cool API. But I had never seen set fit. In fact, another person in engineering had done a prototype customer user journey of trying to train a model with that.