Show HN: Zebrium – ML that catches software incidents and shows you root cause
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Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#2Hey folks,
Larry, Ajay and Rod here!
We're excited to share Zebrium's autonomous incident detection software. Zebrium uses unsupervised machine learning to detect software incidents and show you root cause. It's built to catch even "Unknown Unknowns" (problems you don't have alert rules built for), the FIRST time you hit them. We believe autonomous incident detection is a important tool for defeating complexity and crushing resolution time.
* Get Started *
1) Go to our website and click "Get Started Free". Enter your name, email and set a password. 2) Install our collectors from a list of supported platforms. For K8s it's a one command install. Join the newly created private Slack channel for alerts (or add a webhook for your own) 3) That's it. Automatic incident detection starts within an hour and quickly gets good. You can drill down into logs & metrics for more context if needed.
Getting started takes less than 2 minutes. It's free for 30 days with larger limits and then free forever for up to 500MB/day.
* Here's what you WON'T have to do *
Manual training, code changes, connectors, parsers, configuration, waiting, hunting, searching, alert rules, etc! It works with any app or stack.
* How It Works *
We structure all the logs and metrics we collect in-line at ingest, leverage this structure to find normal and anomalous patterns of activity, then use a point process model to identify unusually correlated anomalies (across different data streams) to auto-detect incidents and find the relevant root-cause indicators. Experience with over a thousand real-world incidents across over a hundred stacks has confirmed that software behaves in certain fundamental ways when it breaks.
It turns out that we can detect important incidents automatically, with a root-cause indicator when the logs and metrics reflect it. Zebrium works well enough that our own team relies on it, and we believe you'll want to use it, too.
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#3Just ran through your intro video. If it does really what it says, this is a great product. I'll have my team test this tomorrow. Good luck on your launch.
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#4Just ran through your intro video. If it does really what it says, this is a great product. I'll have my team test this tomorrow. Good luck on your launch.
thanks Samdung. Appreciate it, and any feedback once you try it.
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#5Very cool, would love something like this. Your video gives a fairly straightforward incident response which traditional tools would work equally well on. Can you describe a situation that Zebrium does better than legacy tools? Perhaps a hypothetical unknown unknown.
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#6Hey folks, Larry, Ajay and Rod here! We're excited to share Zebrium's autonomous incident detection software. Zebrium uses unsupervised machine learning to detect software incidents and show you root cause. It's built to catch even "Unknown Unknowns" (problems you don't have alert rules built for), the FIRST time you hit them. We believe autonomous incident detection is a important tool for defeating complexity and c…
Do you already have data models and ML to handle these tasks today, or are you still building data models with the aid of the clients who sign up for the service?
If you have a functioning platform today, what percentage of events end up needing human escalation?
Asking these questions considering the early stage of your product.
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#7Congrats on the launch. Having worked at a startup in the AIOps space, I can can offer a few suggestions.
1. No matter how good your AI, it will make mistakes. Users need ways to provide feedback or filtering to avoid bad alert fatigue. Giving users a sense of control is critical.
2. Most larger shops will have dozens of monitoring tools already generating alerts. Consider ingesting existing alerts as another algorithmic signal.
3. The real root cause of an incident often won't show up in logs. Don't assume that the earliest event in a cluster is causal.
4. The more context you can provide an operator looking at a potential incident, the better. Modern SEIM tools do an ok job here. Consider pulling in topology or other enrichment sources and matching entity names/IDs to log data.
Good luck. Contact in profile if you'd like to chat further.
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#8As a founder in the monitoring space, and now heading up the core monitoring team at Netflix, I had a chance to work with Zebrium and have to say the technology is impressive. Unlike other anomaly detection services, they've done a lot of work to get decent incidents without too much noise completely unsupervised - this is definitely the next generation of observability and Zebrium has a clear head start in this space!
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#9Very cool, would love something like this. Your video gives a fairly straightforward incident response which traditional tools would work equally well on. Can you describe a situation that Zebrium does better than legacy tools? Perhaps a hypothetical unknown unknown.
Hi, these 2 blogs list some scenarios. The first has more details because we replicated these in house and can share full details:
https://www.zebrium.com/blog/is-autonomous-monitoring-the-an....
https://www.zebrium.com/blog/beyond-anomaly-detection-how-in...
Re: Show HN: Zebrium – ML that catches software incidents and shows you root cause
#10Very cool, would love something like this. Your video gives a fairly straightforward incident response which traditional tools would work equally well on. Can you describe a situation that Zebrium does better than legacy tools? Perhaps a hypothetical unknown unknown.
Hi, these 2 blogs list some scenarios. The first has more details because we replicated these in house and can share full details: https://www.zebrium.com/blog/is-autonomous-monitoring-the-an... . https://www.zebrium.com/blog/beyond-anomaly-detection-how-in...
and although this isn't quite answering your question about unknown/unknowns, this open source project lets you inject failure modes using a chaos tool (litmus) on your own app. We had really good results catching application incidents created by these chaos test.
https://github.com/zebrium/zebrium-kubernetes-demo