Machine Learning: Oversampling vs Sample Weighting

Posted on by David Vassallo

How do you "influence" a ML model? For example, imagine a scenario where you'd like to detect anomalies in a given data set. You reach for your favourite algorithm - in my case Isolation Forest: Our example output from Isolation Forest It does fine for most cases, except that one data point which invariably gets … Continue reading Machine Learning: Oversampling vs Sample Weighting

Speeding up Spring MVC with CompletableFuture

Posted on by David Vassallo

Recently we're beginning to see a shift towards asynchronous/reactive workloads within the Spring ecosystem, especially since the release of WebFlux and the more recent support for Kotlin co-routines. However, both these require moving towards monos, fluxes, and libraries that support these reactive constructs. Support for reactive paradigms is steadily increasing however many of us are … Continue reading Speeding up Spring MVC with CompletableFuture