Now accepting beta users for GPT-3 plain language root cause summary feature.

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This project is a favorite of mine and so I wanted to share a glimpse of what we've been up to with OpenAI's amazing GPT-3 language model. Today I'll be sharing a couple of straightforward results with basic prompts only. There are more advanced avenues we're exploring for our use of GPT-3, such as fine-tuning (custom pre-training for specific datasets); you'll hear none of that today, but if you're interested in this topic, follow this blog for updates.

The promise of tracing

Distributed tracing is commonly used in Application Performance Monitoring (APM) to monitor and manage application performance, giving a view into what parts of a transaction call chain are slowest. It is a powerful tool for monitoring call completion times and examining particular requests and transactions.

Lots of people like to construct dashboards in Grafana, for monitoring and alerting – it’s fast, sleek, and practical. Zebrium is awesome for analytics in-part because we lay everything down into tables in a scale-out MPP relational column store at ingest. Each event type gets its own table with typed columns for the parameters; metrics data is also tabled; ditto for anomalies and incidents.

Automatically Spot Critical Incidents and Show Me Root Cause

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 word “structure”, or why it allows for amazing things like anomaly detection. This is a gentle and intuitive introduction to “structure” as we mean it.

We structure machine data at scale