IBM Watson Visual Recognition (discontinued) vs. Streamlit

As I mentioned before, it can only be employed in simple basic visual recognition applications. It can be employed in large projects and relying it on completely is not encouraged. It's better to create your own algorithms rather than using it. If you are from a non-programming background, then I may suggest you rely on this and use it to develop simple apps that can predict a few plants and animals.

Incentivized

- 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.

Incentivized

• Easy to use interface, you can just drag and drop the images in the negative and positive dropboxes to train.
• It's affordable and there is a free version to test for yourself and check if it's useful for you.
• Easy to integrate with apps using one single API key and you can train easily with your terminal.

Incentivized

• Incredibly Easy
• Customizable
• Quick and powerful

Incentivized

• Documentation sometimes not complete

Incentivized

• 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.

Incentivized

I am giving this rating on the basis of its usability in real-time applications and based on the interface to upload negative and positive images to train the AI. But it's not perfect and sometimes its predictions are wrong. On overall usability, it's better if you are planning on working with UI rather than using complex programs and algorithms on your own.

Incentivized

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.

Incentivized

• High cost if models are trained

Incentivized

• 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.

Incentivized