With the surge in AI tools the past few months, it’s never been easier to build your own AI systems, but getting people to use it? That’s a whole different ballgame. And here’s the catch, if your model isn’t running inference, it’s not making an impact. Enter LandingLens and Streamlit. LandingLens is an all in one AI vision system platform where you can label, train and deploy models in minutes. Streamlit is a Python library that is open-source, providing a seamless way to develop and distribute interactive web applications and data visualizations. By offering an open-sourced app framework, Streamlit empowers Machine Learning and Data Science teams to quickly pull together user interfaces (UIs) to host their AI applications. External users can navigate to a specific URL, upload some images and get information generated by the AI back. By democratizing access to the AI system, AI teams are scaling usage of their apps and seeing bigger, more cross-functional impact from their work.
Imagine you’re crafting an app that’s a game-changer in cancer diagnoses. With LandingLens, you train AI models to spot cancerous cells and deploy it to a cloud endpoint. Then using Streamlit, you build an easy to use UI that allows any doctor to regularly upload folders of pathology images for cancer screenings. These images are tagged with metadata based on how confident the model is in it’s cancerous/benign diagnoses. As an output, pathology teams receive a folder of images organized based on cancer severity, allowing them to quickly focus on the more severe cases first.
With LandingLens and Streamlit, you’re not just building; you’re creating user-friendly experiences that are transforming industries, from healthcare to manufacturing. Want to try it yourself? Sign up for LandingLens, grab your API key and try our latest Streamlit x Landing AI OCR app.
Streamlit.io
Streamlit is a Python library that is open-source, providing a seamless way to develop and distribute interactive web applications and data visualizations. With Streamlit, you can effortlessly create web apps using Python code. The library comes equipped with integrated support for various data visualization libraries such as Matplotlib, pandas, and plotly, simplifying the process of generating interactive charts and graphs that dynamically update based on user input. It is a popular tool among data scientists, machine learning (ML) engineers and developers looking to share interactive web apps with their audience.