Streamlit: the fastest way to make your models usable (and beautiful)
You’ve built the machine learning (ML) model. It works. Now the business wants to see it. But here’s the catch – they don’t want a notebook, a SQL dump, or a chart in Looker. They want something they can interact with. That’s where Streamlit comes in.
At EdgeRed, we’ve been using Streamlit to quickly turn machine learning models and data prototypes into simple, usable apps – especially for internal stakeholders who don’t want to see code, but do want to play with inputs and get results.
What is Streamlit?
Streamlit is a Python-based open-source framework for building interactive web apps for data science and machine learning – without needing to learn frontend code.
You write a script in Python, sprinkle in a few Streamlit components like sliders, dropdowns or buttons, and it instantly spins up a clean, functional app. No HTML. No JavaScript. Just Python.
Why we like using it
1. It’s fast to build
You can go from “I’ve got a model” to “Here’s a working app” in hours. We’ve used it to prototype internal tools, showcase models, and collect stakeholder feedback – all without needing a product team.
2. It’s great for non-technical users
If your audience doesn’t want to read a Jupyter notebook or inspect model coefficients, Streamlit gives them a simple UI to interact with. We’ve built apps where users adjust sliders, change input parameters, and instantly see updated results – no SQL or code required.
3. It looks clean out of the box
The design is minimal, modern, and doesn’t need tweaking to look decent. That’s a plus when you’re demoing to execs and want to keep things sharp.
4. Great for internal prototypes and demos
We’ve used Streamlit with stakeholders from property to finance teams – to help them test different model scenarios and understand the output in context. It’s especially useful when you need feedback before committing to a production build.
Real use case: interactive model apps for internal teams
One of our recent projects involved surfacing machine learning model results for a client’s internal team. The model predicted outcomes based on multiple business inputs – but most of the team didn’t have access to the Snowflake backend, and weren’t technical enough to run queries.
So we built a Streamlit app that let them:
- Select input variables via sliders and dropdowns
- Trigger machine learning predictions in real time
- View charts and key metrics in a clean UI
The result? Faster feedback, better understanding of the model, and fewer email threads asking “can you send me a screenshot of X?”
What to watch out for
Streamlit is great – but it’s not a full-featured web platform. Here are a few limitations we’ve run into:
- You’ll still need access to your data – in our case we hosted on a data warehouse so users needed Snowflake/Databricks accounts, which added some friction
- Not ideal for large scale production apps – it’s perfect for prototypes, internal demos, and apps with a small user base, but if you’re building a large scale client-facing product, you’ll hit limitations around scaling and flexibility
- Python-only – which is fine for us, but worth noting for mixed-language teams
“Within 4 hours, we built a Streamlit demo app connected to our machine learning models, enabling business stakeholders to input parameters seeing real-time predictions. This interactive demo provided immediate clarity on model behavior and accelerated stakeholder buy-in for production deployment.”
– Jack N, Senior Data Analyst at EdgeRed
When we recommend it
Use Streamlit when:
- You want to prototype an idea quickly
- Stakeholders need to interact with a model without touching code
- You need a lightweight internal tool to explore results or run simulations
- You’re working in Python and don’t want to build a whole front-end
It’s a low-effort, high-impact way to turn models into something people can actually use.
Final thoughts
Streamlit helps bridge the gap between data teams and decision-makers. It’s fast to build, easy to use, and makes your models feel less like code and more like tools.
At EdgeRed, we’ve used it to get feedback early, keep stakeholders engaged, and move projects forward without waiting for a dev team. It’s not for every use case – but when you just want to get something in front of people, it’s hard to beat.
If you’re looking for ways to make your models more visible (and useful) across the business, Streamlit might be your new secret weapon.
This blog is written by Jack, assisted by E.R.I.C.A.
About EdgeRed
EdgeRed is an Australian boutique consultancy with expert data analytics and AI consultants in Sydney and Melbourne. We help businesses turn data into insights, driving faster and smarter decisions. Our team specialises in the modern data stack, using tools like Snowflake, dbt, Databricks, and Power BI to deliver scalable, seamless solutions. Whether you need augmented resources or full-scale execution, we’re here to support your team and unlock real business value.
Subscribe to our newsletter to receive our latest data analysis and reports directly to your inbox.
