10-23, 17:00–17:30 (US/Eastern), Main Room
The collection and storage of observability data is critical for day to day operations and long term health of clusters and applications. The increasing volume of this observability data can be leveraged by AI algorithms and data analytics to automate triaging, response, and remediation for common issues, reducing mean time to detection and resolution. To achieve this observability based AIOps system, one must be capable of implementing AI algorithms, set up a combination of logging, monitoring, and tracing backends to store data, and agents for each type of observability data in downstream clusters to ship data to the backend. This complex setup can be challenging to users and this talk will demonstrate how Opni can be leveraged to simplify the setup and management of a fully open source AIOps & observability system.
There are many open source options for logging, monitoring and tracing. Users must set these up individually to collect all 3 types of observability data. The creation and management of these tools is often challenging and can be simplified. In addition to this, to leverage AIOps users must be knowledable with GPUs as well as machine learning and deep learning algorithms. Opni was created to address these challenges and offer an observability management tool that comes with AIOps baked in. Opni is the first open source AIOps tool that offers easy creation and management of logging, monitoring, and tracing backends. It leverages and extends upstream open source projects including OpenSearch, Cortex, OpenTelemetry and others!
Started Opni at SUSE Rancher and is the engineering manager for the project. Experience in various AI & software projects at big tech companies (IBM, Disney, Amazon). From Cupertino, CA and enjoys playing basketball, tennis and golf. Interested in finance, politics, and black holes.