Category: Zebrium

At Zebrium, we have a saying: “Structure First”. We talk a lot about structuring because it allows us to do amazing things with log data. But most people don’t know what we mean when we say the “structure”, or why it is a necessity for accurate log anomaly detection. Our Vision of Log Anomaly Detection… Continue reading Log Anomaly Detection Using Machine Learning

Datadog is one of the most popular observability platforms today and offers a rich set of capabilities including monitoring, tracing, log management, as well as machine learning (ML) features that help detect outliers. One of its most interesting feature sets falls under the Watchdog umbrella. Watchdog Root Cause Analysis Watchdog automatically detects outliers in metrics… Continue reading Zebrium RCaaS: A Natural Evolution From Datadog Watchdog Insights Log Anomaly Detection

There’s a good reason Datadog is one of the most popular monitoring solutions available. The power of the platform is summed up in the tagline, “See inside any stack, any app, at any scale, anywhere” and explained in this chart: “Datadog brings together end-to-end traces, metrics, and logs to make your applications, infrastructure, and third-party… Continue reading Using Datadog For Observability? Speed up Troubleshooting with Zebrium

Application monitoring is experiencing a sea change. You can feel it as vendors rush to include the phrase “root cause” in their marketing boilerplate. Common solutions enhance telemetry collection and streamline workflows, but that’s not enough anymore. Autonomous troubleshooting is becoming a critical (but largely absent) capability for meeting SLOs, while at the same time,… Continue reading Observability: It’s Time to Automate the Observer

Native machine learning for ElasticSearch was first introduced as an Elastic Stack (ELK Stack) feature in 2017. It came from Elastic’s acquisition of Prelert, and was designed for anomaly detection in time series metrics data. The Elastic ML technology has since evolved to include anomaly detection for log data. So why is a new approach… Continue reading Elasticsearch Machine Learning -An Improved Approach Using Correlated Anomaly Detection To Find Root Cause

When a new/unknown software problem occurs, chances are an SRE or developer will start by analyzing and searching through logs for root cause – a slow and painful process. So it’s no wonder using machine learning (ML) for log analysis is getting a lot of attention. But machine learning (ML) with logs is hard. Here’s… Continue reading Log Analysis with Machine Learning: An Automated Approach to Analyzing Logs Using ML/AI

If you are a New Relic user, you’re likely using New Relic to monitor your environment, detect problems, and troubleshoot them when they occur. But let’s consider exactly what that entails and describe a way to make this entire process much quicker. Imagine that the dashboards used to monitor your application suddenly show a “blip”.… Continue reading Using New Relic For Observability? Speed up Troubleshooting with Zebrium

The Elastic Stack (often called ELK) is one of the most popular observability platforms in use today. It lets you collect metrics, traces, and logs and visualize them in one Kibana dashboard. You can set alerts for outliers, drill down into your dashboards and search through your logs. But there are limitations. What happens when… Continue reading Using the Elastic Stack (ELK) For Observability? Here’s How to Speed Up Troubleshooting

A few weeks ago, Larry Lancaster, wrote about a new beta feature leveraging the GPT-3 language model – Using GPT-3 for plain language incident root cause from logs. To recap – Zebrium’s unsupervised ML identifies the root cause of incidents and generates concise reports (typically between 5-20 log events) identifying the first event in the… Continue reading Real World Examples of GPT-3 Plain Language Root Cause Summaries