impresburger

5 ms·

1.
by impresburger 1y ago
I think you're probably right. As an example of this challenge, I've noticed that engineers who don't have a background in ML often lack the "mental models" to understand how to think about testing ML models (i.e. s
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by impresburger 1y ago
Interesting! We don't currently support this explicitly. You could wrap the Plexe-built model in a scikit-learn Estimator like I mentioned, and you can specify the desired input/output schema of the model when you start building i
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by impresburger 1y ago
No, not by default. In fact, the default installation of plexe doesn't include deep learning libraries. Plexe _can_ build deep learning models using `torch` and `transformers`, and often the experimentation process will include some NN
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by impresburger 1y ago
Do you mean being able to wrap the created model in a scikit-learn Pipeline? This isn't something we've thought about and we haven't explicitly built support for it, though we could. As of now, I think you could relatively ea
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by impresburger 1y ago
Agree. We've designed a mechanism to enable any of the agents to ask for input from the user, but we haven't implemented it yet. Especially for more complex use cases, or use cases where the datasets are large and training runs ar
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by impresburger 1y ago
Just to clarify, we're not directly using the LLMs as the "predictor" models for the task. We're making the LLMs do the modeling work for you. For example, take the classic "house price prediction" problem. We
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by impresburger 1y ago
Currently it decides whether to make its own model or fine-tune a relevant one based primarily on the problem description. The agent's ability to analyse the data when making decisions is pretty limited right now, and something we&#x27
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by impresburger 1y ago
Hey, this is super cool! We found a few projects working on similar things to Plexe, but were not aware of yours. Thanks for sharing, will check it out!
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by impresburger 1y ago
Hey, one of the authors here! I completely agree with your comment. Training ML models on a clean dataset is the "easy" and fun part of an ML engineer's job. While we do think our approach might have some advantages compared
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by impresburger 1y ago
Hey, I'm one of the authors of Plexe. Overall, I'd say we like smolagents: it's simple, easy to understand, and you can get a project set up very quickly. It also has some neat features, such as the "step callbacks"