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Intuit runs ~2500 services on Kubernetes, and being one of the top SAAS companies; operational excellence is a top priority. While considerable effort and cost were spent on all three pillars of observability (metrics, log, events), there was a gap in identifying customer impact and causal service to improve MTTD/R for our services. The challenge is that we need to analyze massive amounts of data generated by these data sources in real-time at scale, which is a beast of a problem. Our talk focuses on using Numaproj (Intuit’s open project) and other CNCF open-source technologies to address this problem. Numaproj includes Numaflow, a stream processing platform, and Numalogic, a collection of ML models. We will show how we collect, process, and analyze data per minute in real-time and how using Numaproj, we compute normalized anomaly scores for every data point. These anomaly scores helped us weed the noises out from the data, provide a high signal-to-noise ratio, and directly create incidents. Today we detect incidents based on the scores (~98% confidence) generated by our AIOps platform. With this solution, Intuit has established a cookie-cutter to reduce its MTTD from over 30 minutes to less than 3 minutes.
Amit is Director of Engineering at Intuit responsible for Observability and Analytics which covers Observability , Operational Data Lake and Platform Analytics. He is also responsible for open source projects Argo and Numaproj. He is currently working to deliver observability and... Read More →
Vigith is a Principal Software Engineer for the Intuit Observability and Analytics team in Mountain View, California. One of Vigith's current day-to-day focus areas is on the various challenges in building scalable, data and AIOps solutions for both batch and high throughput systems... Read More →
