Abstract: Scalable Machine Learning at Yahoo
Yahoo scientists have developed variety of machine learning libraries (supervised learning, unsupervised learning, deep learning) for online search, advertising and personalization. The emerging business needs require us to address 2 problems:
- Can we apply these libraries against massive datasets (billions of training examples, and millions of features) using commodity hardware clusters?
- Can we reduce the learning time from days to minutes or seconds?
We have thus examined system architecture options (including Hadoop, Spark and Storm), and developed a fault-tolerant MPI solution that allows hundreds of machines to jointly build a model. We are collaborating with open source community for a better system architecture for next-gen machine learning applications. Yahoo ML libraries are being revised for much better scalability and latency. In the talk, we will share system architecture of our ML platform and its use cases.