I'm currently doing the "develop in jupyter notebooks, deploy in python scripts" thing, however I'm only deploying predictive models, using AWS Lambda.
Would Streamlit be a good fit for this? I can see the value of the inline visualisation for code demos, testing, etc but it is not clear how I would then go about deploying any part of this into production ML environment, where the "data vis" part is less useful.
I'd love something that allowed me to deploy some code to Lambda, while also deploying Streamlit as a "swagger docs" type of explanation/exploration tool showing how what I've built works.
I'm going to try and build this into my current deployment, but I don't see anything on Streamlit in terms of an opinion on "deployment" concepts/options.
I've never used Lambda, but your question prompted two thoughts:
(1) Streamlit has an interesting property which we haven't yet publicized which is that if you:
python a_streamlit_sctipt.py
instead of
streamlit run a_streamlit_sctipt.py
It runs a_streamlit_sctipt.py from top to bottom but disables all the Streamlit code! This is intended so that Streamlit scripts can doouble-duty as both ordinary python scripts and inline visualizations. Now. I'm not sure how this would behave with lambda, but I'd be very curious to hear your experience.
(2) The more standard Streamlit approach would be to deploy your model to lambda, and then write a Streamlit app which connects to that model on lambda and visualizes your model.
I hope that helps. Please do share your experiences! Streamlit is an emerging technology and we're still very much figuring out how it fits in the ecosystem. I think figuring out Streamlit <-> Lambda is important and I very curious to hear what you find!
Btw, the best place to continue the conversation would be at discuss.streamlit.io because there are probably more people there with Lambda experience who could help you. Also, more Streamlit users could benefit from your insights. :)
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I'm currently doing the "develop in jupyter notebooks, deploy in python scripts" thing, however I'm only deploying predictive models, using AWS Lambda.
Would Streamlit be a good fit for this? I can see the value of the inline visualisation for code demos, testing, etc but it is not clear how I would then go about deploying any part of this into production ML environment, where the "data vis" part is less useful.
I'd love something that allowed me to deploy some code to Lambda, while also deploying Streamlit as a "swagger docs" type of explanation/exploration tool showing how what I've built works.
I'm going to try and build this into my current deployment, but I don't see anything on Streamlit in terms of an opinion on "deployment" concepts/options.