Spotting the Exception: Classical Methods for Outlier Detection in Data Science

Outliers are unique in that they often don’t play by the rules. These data points, which significantly differ from the rest, can skew your analyses and make your predictive models less accurate. Although detecting outliers is critical, there is no universally agreed-upon method for doing so. While some advanced techniques like machine learning offer solutions, …

SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking

In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, …

How to mitigate the risks of DIY authoritative DNS

While many network admins outsource the management of authoritative domain name system (DNS) infrastructure to a third party like IBM® NS1 Connect®, there is a sizable community of network operators who prefer to dig in and build something themselves. These do it yourself (DIY) authoritative DNS architectures can be cobbled together from various tools. BIND is most …

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

This is a guest post written by Axfood AB.  In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. Axfood is Sweden’s second largest food retailer, …