Machine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful.
Recently, there has been an emergence of several tools that Data Scientists can use to quickly and easily deploy a machine learning model. In this article, we’re going to look at 4 alternatives that you can use to deploy a machine learning model: Gradio, Streamlit, Dash, and Flask.
Keep in mind that this is an opinionated article and is solely based off of my knowledge and experiences with these tools.