Unlocking insights: How to analyze Strava data with Elastic AIOps

The blog post delves into analyzing Strava data using Elastic AIOps, focusing on new machine learning features such as anomaly detection for fitness activities. It highlights the integration of custom models and built-in functions, like explaining log rate spikes, which help identify deviations in athletic activities. For instance, the post demonstrates using histograms to detect differences in activity types over time, such as an increase in virtual rides during winter months. Further, the post explains creating a single metric job to track anomalies in distance covered, utilizing a Strava index transformed into mean distance metrics, and analyzing daily data for outliers. The process involves setting up a model to predict future activity levels, with the ultimate goal of surpassing these predictions as a form of personal challenge. The content also encourages readers to explore their Strava data and suggests resources for further learning about machine learning with the Elastic Stack, while promoting a free trial of Elastic Cloud.