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I'm confused about the ipython reference, using a notebook for eda or results presentation doesn't have much to do with a data pipeline. I love the idea of bein
by version_five 5y ago
I'm confused about the ipython reference, using a notebook for eda or results presentation doesn't have much to do with a data pipeline. I love the idea of being able to use shell scripts to orchestrate data prep and training. There are lots of
curl | grep | awk > .csv
patterns I've used in real life, and having a way to train a model from the command line is a great complement.
Currently I would call a python training script from the command line, and do orchestrate everything in bash. I'm not sure why thay is undesirable.
- isabellat 5y agoYes! We want to make the whole ML process from training to deployment to monitoring radically more convenient. The CLI interface makes it easy to quickly train ML models without ever opening a Jupyter notebook, learning what pandas and numpy are etc. And after you’re done training, we want you to be able to incorporate your models into your existing stack: we have language libraries for programming languages traditionally underserved by the current ml tools: ruby, golang, elixir, nodejs, rust. (We also have python). All prediction happens in process with no network requests, meaning you don’t have to deploy a separate service to host your ml models :)