Speaker: Adam Drake
Datasets too large to fit into RAM are increasingly common, even in simple environments like Kaggle competitions. Adam will introduce some ways of dealing with this issue in addition to demonstrating some scalable machine learning techniques which are production ready and capable of processing over 10s of thousands of events per second on an old laptop.
I have been in technology roles in a variety of industries, including online marketing, financial services, healthcare, and oil and gas. My background is in Applied Mathematics, I help companies change and use data more effectively, and my technical interests include online learning systems, high-frequency/low-latency data processing systems, recommender systems, distributed systems, and functional programming.
Produced by Engineers.SG
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