One grossly under-emphasised skill for ML projects for both the Data Scientist and the team, is quick, interactive prototyping of an idea.

Five years ago building web services and apps for solely demo purposes of ML tools was painful and less-accessible, often requiring more unicorn data scientists with hack-y full-stack skills. This has dramatically changed in the past couple of years with beautiful efforts and frameworks to help build quick web apis for your ML models (e.g. FastAPI), and also interactive web app prototypes for your ML model or pipeline (e.g.: Streamlit, Gradio) in next to no time.

Why is this important?

• setting projects up for success with better communication of ideas to stakeholders

• helps in extracting feedback early in project from stakeholders (decision makers + users/customers)

• building end to end prototypes tests ideas and holes, and can make iterations stronger and faster

• better communication b/w cross-disciplinary team members - UX, data scientists. engineers, product managers

• can reduce time to production greatly

  I have lost count of the number of times a certain ML project either does not move past the first experimental models, or moves too slowly. Much too often we only see lifeless tables of precision recall values, or slideware telling the story of the Data Science endeavour/intervention. Undoubtedly these tables store the most value and are the output of great skill+time to build and test ML models. However, would it not be transformative if we could bundle that sentiment classification model as a web service and expose it over an api to demonstrate its value to stakeholders? Or if we could have prospective users clicking on the cool Generative AI (ChatGPT-esque) tool to get feedback from users? I have found prototyping tools to be the most amazing research aids to ensure that what we build is what users need and want. It inspires the team and keeps the idea flywheel whirring.

My two favourite tools here would be Streamlit, and FastAPI - and I think they are a good place to start, with active communities and great tutorials.

This is not an exhaustive list, and there are many great tools out there for you to try. Essentially, search for tools and frameworks that help in building web apis and apps for demoing and testing ML capabilities.

Try it (if you haven't already), and you will see how being able to quickly deploy interactive apps and services that bring your demo Machine Learning capability to life is a real game changer.