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Beeswax uses H2O machine learning to power low-latency, high-throughput digital ad campaign optimization.
Brands are demanding advertising objectives that are more meaningful to them. It is now possible to tie online advertising to offline purchases. As more facets of advertising and attribution can be measured, it becomes important for brands to take control of their own optimization.
“H2O has the experts on their team that can help us. That makes my life easier because people on my team can focus on the business needs.”Sergei IzrailevChief Data Scientist
Real-time systems such as those deployed in online advertising impose stringent constraints on the design of machine learning solutions. Digital ad campaign optimization engines typically need to obtain machine learning predictions for a given ad within a few milliseconds. At the same time, the number of ads ranges from tens of thousands to millions per second. This presentation describes general design patterns that tend to work well in such an environment and illustrates how real-time constraints shaped machine learning system design decisions at Beeswax.
“Digital ad campaign optimization engines need to obtain machine learning predictions within a few milliseconds.”Sergei IzrailevChief Data Scientist