Streamlit links from Pocket
One of the main features of Streamlit is it provides you a Jupyter Notebook-like environment where your code is updated live as you save your script. This helps a lot, especially for the initial app development stage.
Sadrach Pierre, Ph.D. Companies have great interest in clearly communicating their ML-based predictive analytics to their clients. No matter how accurate a model is, clients want to know how machine learning models make predictions from data.
Sometimes you make a data science , machine learning or computer vision projects but suddenly you stuck with a thought that how to show it to the world, what type of clear technology I can use, so that everyone can see and understand your model. Then here comes the Streamlit web framework.
Now that you’ve created your app, you’re ready to share it! Use Streamlit sharing to share it with the world completely for free. Streamlit sharing is the perfect solution if your app is hosted in a public GitHub repo and you’d like anyone in the world to be able to access it.
Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. In just a few minutes you can build and deploy powerful data apps - so let’s get started! Make sure that you have Python 3.6 - Python 3.
Streamlit is an awesome new tool that allows engineers to quickly build highly interactive web applications around their data, machine learning models, and pretty much anything. The best thing about Streamlit is it doesn’t require any knowledge of web development.
Streamlit is an app framework to deploy machine learning apps built using Python. It is an open-source framework which is similar to the Shiny package in R. Heroku is a platform-as-a-service (PaaS) that enables deployment and managing applications built in several programming languages in the cloud.
Streamlit is an app framework to deploy machine learning apps built using Python. It is an open-source framework which is similar to the Shiny package in R. This article assumes the reader to have basic working knowledge of Conda environment, Git and machine learning with Python.
If you’re a data scientist or a machine learning engineer, you are probably reasonably confident in your ability to build models to solve real-world business problems.
I’ve always wanted to host some of my data science projects to the web so that my models could be interactive and have a greater reach.