Likelihood to Recommend They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to
TensorFlow (
Keras ), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
Read full review - Don't want to pay
Tableau $1,000 / seat? Use Streamlit - Want fully custom views and navigation? Use Streamlit - Want access to Machine Learning and not just your dev team? Use Streamlit - Want to keep things internal and secure? Use Streamlit - Want your Data Science team to be able to crank out projects quickly? Use Streamlit - Sick of Jupyter Notebooks and Business Leaders not understanding them? Use Streamlit Our D.S. strategy has moved completely to delivering pages in Streamlit. I can hand an executive a Jupyter notebook and it'll get lost in translation. I can give them sign-in access to a page and they can answer all of their own "What-If?" questions! We've used Streamlit to productize our Data Science and Machine Learning capabilities.
Read full review Pros Provides Benchmark datasets to test your custom algorithm Provides with a lot of pre-coded neural net components to use for your flow Gives a framework to write really abstract code. Read full review Incredibly Easy Customizable Quick and powerful Read full review Cons Distributed data parallel still seems to be complicated Support for easy deployment to servers Torchvision to have support for latest models with pertained weights Read full review Recent Security issues (they quickly released an update to combat this though...) Requires a bit of HTML knowledge to really customize. If you're going quick, you don't need HTML though. Streamlit commands will pump your page out fast. Read full review Alternatives Considered As I described in previous statements, Pytorch is much better suited than
TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks. You can get to better performance models by better understanding the deep learning model code, so I think the choice of Pytorch is easy and simple.
Read full review I started using Streamlit when it first came out and thought it was really useful and powerful. A few years later and they've really hit their stride! The features / widgets / materials they provide have been well researched, well designed, and well implemented. I will take Streamlit to any future companies I go to as well as be a strong promoter wherever I'm currently at. It's free. It's easy to use. It is really powerful. Sure? You could go pay for a larger system but your Data Science team should be able to handle Streamlit easily. I'd argue a non-technical person spending a few weeks in python could pick up Streamlit really quickly.
Read full review Return on Investment I'd estimate I can build a model 50% faster on pytorch vs other frameworks Read full review I've scaled my team 2x since using Streamlit. We show off actual results that users can play with We're building a customer facing page that we're going to monetize. Incredible amounts of visibility into my team and what we're accomplishing. Read full review ScreenShots