Since generative AI exploded, it's all anyone talks about. But traditional ML still covers a vast space in real-world production systems. I don't need this tool right now, but glad to see work in this area.
"classical ML" models typically have a more narrow range of applicability. in my mind the value of ollama is that you can easily download and swap-out different models with the same API. many of the models will be roughly interchangeable with tradeoffs you can compute.
if you're working on a fraud problem an open-source fraud model will probably be useless (if it even could exist). and if you own the entire training to inference pipeline i'm not sure what this offers? i guess you can easily swap the backends? maybe for ensembling?
if you're working on a fraud problem an open-source fraud model will probably be useless (if it even could exist). and if you own the entire training to inference pipeline i'm not sure what this offers? i guess you can easily swap the backends? maybe for ensembling?
Why not a typical shared library that can be loaded in python, R, Julia, etc., and run on large data sets without even a memory copy?
Don't understand here the parallel.