Yes, this is the issue. In any reasonably-sized enterprise you’re not going to have a clean CSV to plug in to a model generator. You’re either going to have 1) 50 different excel spreadsheets to wrangle and combine somehow or 2) 50+ terabytes of messy logs to process.
Creating something that can grok MNIST is certainly cool, but it’s kind of the equivalent of saying leetcode is equivalent to software engineering.
Second, and more practically speaking, you are automating (what I think of as) the most fun part of ML: the creativity of framing a problem and designing a model to solve that problem.
Agree completely. We built Plexe with that first scenario in mind - the messy spreadsheet problem that's so common in enterprise. You can connect multiple data sources, and Plexe will identify what it needs based on the problem description. We're also gradually developing support for handling terabyte-scale data, though we're not there yet. We started by validating our approach on well-defined problems with clean datasets, but we've been systematically adding capabilities to handle increasingly complex scenarios.
On your second point about automating the "fun part", we see Plexe as amplifying that creativity rather than automating it. We're trying to make it easier to design the experiments and evaluating results. But would love to hear your feedback on this!