I tried using this today and it's really slick. The API and the ease of implementation on OSx and Ubuntu were surprising.
Streamlit launches open-source machine learning application dev framework
11–20 of 21 posts
Re: Streamlit launches open-source machine learning application dev framework
#12This looks really cool! We've been using Plotly's Dash framework for about a year and a half at work and its been fantastic and only getting better. I do like the idea of not having callbacks here, though I've started to get a lot more comfortable with it and it feels a bit more robust. At first glance, this feels like a competitor to Dash, but after chatting about this with my team, we realized it actually is closer…
Hey, another co-founder of Streamlit here. Let us know how this works out for your team!
And for the nerdier people on your team, they may appreciate our post in Towards Data Science today: https://towardsdatascience.com/coding-ml-tools-like-you-code...
Re: Streamlit launches open-source machine learning application dev framework
#13Hi. I'm Adrien, co-Founder and CEO of Streamlit. For more information, please check out our launch post: https://towardsdatascience.com/coding-ml-tools-like-you-code... I'm happy to answer any questions you have!
It looks great and I am excited to try it. My only concern is the business model....because it is not clear what that it. Can you enlighten us?
Re: Streamlit launches open-source machine learning application dev framework
#14[deleted]
Re: Streamlit launches open-source machine learning application dev framework
#15Nice, this looks well thought out, thanks! Haven't played with it yet, but it looks like it can be used for 'normal' data science stuff as well as machine learning, is that right?
Re: Streamlit launches open-source machine learning application dev framework
#16Can I bring in my own css?
Re: Streamlit launches open-source machine learning application dev framework
#17Nice, this looks well thought out, thanks! Haven't played with it yet, but it looks like it can be used for 'normal' data science stuff as well as machine learning, is that right?
Yup definitely! We'd love to discuss with you more on our community forum https://discuss.streamlit.io .
Re: Streamlit launches open-source machine learning application dev framework
#18Hi. I'm Adrien, co-Founder and CEO of Streamlit. For more information, please check out our launch post: https://towardsdatascience.com/coding-ml-tools-like-you-code... I'm happy to answer any questions you have!
It looks great and I am excited to try it. My only concern is the business model....because it is not clear what that it. Can you enlighten us?
Open-source Streamlit (github.com/streamlit) free forever. Our business model is to charge for our the enterprise version, Streamlit for Teams (streamlit.io/forteams), which provides one-click deploy, on-prem hosting, easy sharing, access controls, and other "pro" features. Please contact us if you're interested in these features!
Re: Streamlit launches open-source machine learning application dev framework
#19Hi. I'm Adrien, co-Founder and CEO of Streamlit. For more information, please check out our launch post: https://towardsdatascience.com/coding-ml-tools-like-you-code... I'm happy to answer any questions you have!
Hi Adrien
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.
Re: Streamlit launches open-source machine learning application dev framework
#20Hi. I'm Adrien, co-Founder and CEO of Streamlit. For more information, please check out our launch post: https://towardsdatascience.com/coding-ml-tools-like-you-code... I'm happy to answer any questions you have!
Hi Adrien 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 us…
Hey Sails. This is a great question!
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. :)