She could train on a shared university cluster, but that might take months.

These roadblocks risk jeopardizing her ability to finish the paper in time, and her two options don’t seem like they will allow her to meet her deadline: Despite her rapidly approaching deadline, she hasn’t been able to find the right hyperparameters to train her model on, and has no idea how to share her work with others in her lab. Recently, she began writing a paper for an upcoming conference. Jane is a researcher working on computer vision for robotic.

To get a better sense of how Lightning accelerates machine learning development, let’s walk through a few use cases. This is possible whether you’re doing research, model building and training, pushing to production, deploying to a public cloud, or even keeping your Lightning App private on your own cluster. These constantly growing App and Component galleries allow you to piece together whatever you need to create a machine learning system. Because they’re all open source, you can download the code and modify it in whatever way you need to fit your unique use case. One of Lightning AI‘s core experiences is our gallery of open-source Lightning Apps and Components that you can use to extend your research and build it into an end-to-end ML system in minutes. You can watch her presentation below, or continue reading to learn how Lightning AI simplifies this process, allowing you to create a fully interactive application in a matter of days - not months.

At June’s inaugural Lightning Developer Conference, Lightning AI’s Staff Product Manager Eden Afek explored how you can bring your machine learning models to life through AI-powered Lightning Apps.

How to open lightning app builder