Yufeng has been building computers since elementary school and lives at the intersection of hardware and software. As a Google Developer Advocate, Yufeng bridges the gap between the developer community and engineering teams. He is interested in combining IoT devices, big data, and machine learning, and loves learning new technologies. When he's not tinkering, you can find him running on the road or the track training for his next race. Previously, he was a software developer at Bloomberg where he wrote custom CRM software and equity research tools. Yufeng earned a bachelor's degree in Biomedical Engineering with a concentration in Sensors and Instrumentation from Johns Hopkins University, where he graduated with departmental honors.
When running machine learning at scale, there are many challenges that are encountered. From pulling in large volumes of data to running machine learning across multiple machines, we will walk through the options available and how to implement solutions that allow for scalable machine learning. Operating in a cloud native environment to move data around and load and process it, we will go from source data to insights in the span of 60 minutes.
Specifically, we will use Tensorflow to build a machine learning model and train in the cloud. Then we will also show deploying the trained model such that it can be useful to end users.
This talk is intended for developers and technologists who want to learn about machine learning in production environments. You will get more out of this talk if you have a basic understanding of machine learning.
The entranceways to the dorms at Hogwarts were guarded by paintings that talked and asked for passwords. In this talk, see a real-world Harry Potter style personal 'security' system that can understand spoken passwords and see the people and objects that wish to gain entrance. Learn about how machine learning APIs will enable the next generation of smart devices, and explore other applications of machine learning in the real-world, both in present and in the future.