Our workshops give engineers the tools to start using machine learning in their projects. We also provide a series of more advanced training courses for machine learning practitioners who want to hone their skills and learn new approaches. Check out some of our upcoming events.

Open ML Hackathon
5775 Morehouse Drive, San Diego
July 25 from 5 to 9pm
The Open ML Meetup, Qualcomm, and Weights & Biases have teamed up to bring you a fun-filled evening competing in a deep learning competition. The competition will be led by Lukas Biewald, CEO & Founder of Weights & Biases. Prizes will be awarded to the top 3 teams as well as for most creative solution! Prerequisites - Be familiar enough with Python to install libraries and run code - Have taken an online ML course or trained a model before - If you're unsure, run through this quick tutorial.

Register on Eventbrite as well as filling out this required Qualcomm application: Required Application
ML Competition
Figure Eight, 940 Howard Street, San Francisco
July 24 from 5:30 to 9:30pm
Deep Learning is the biggest change happening in computer science right now. It powers everything from Google’s AlphaGo to Tesla’s autopilot to Amazon’s Echo. Every engineer is trying to figure out how to gain skills in deep learning before they are left behind. Deep learning makes all kinds of new applications possible but presents a whole new set of challenges like exotic hardware and non-determinism. That’s part of the reason why companies can’t hire experts fast enough. We strongly believe you don’t need a degree from Stanford or MIT to build your own algorithms and use this amazing technology. Figure Eight has designed our machine learning training curriculum so that every engineer has access to cutting-edge knowledge and training that you can take back to your work and projects. While there are plenty of online resources, we know it's tough to learn a technical topic without support. We're bringing together engineers who are interested in learning about deep learning and creating a hands-on approach.