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Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

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Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#31

Post by zhangwins »

knightelvis wrote: post #30

Without looking into details of your soluton, what's the difference between your solution and Cloudwatch anomaly detection? https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitori...

Orbiter anomaly detection is for any DB (e.g. Postgres, Snowflake) and metrics that business/product teams tend to track such as transaction conversion %, user growth, add item to basket %, etc.

Amazon Cloudwatch anomaly detection is for AWS resources & apps, and covers infra metrics like resource utilization, app performance, ops health.

In terms of the anomaly detection capabilities -- both are using similar machine learning processes to detect metric issues automatically!

P.S. If you get curious about the details of our solution, we have a 2 minute video demo ;) Cheers! https://www.youtube.com/watch?v=R7P_M6j0P2A

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#33

Post by jacques_chester »

generatorguy wrote: post #10

I work on power stations which normally have about 1000 monitored variables per turbine-generator and another 500 for the plant in general. So typically 2500 for a two unit plant. Alarms are generated if a variable exceeds a threshold, or a binary variable is in the wrong state. Is Orbiter something that would benefit power plants?

I think some sort of anomaly detection would be useful in your case. There are a bunch of libraries floating about, I remember at least Netflix[1], Yelp and Datadog talking about them. There appears to be a really good links page available too[1]. You can also learn a lot from Forecasting Principles and Practice, which is free online[2]

I have previously pitched using a kind of SPC-for-metrics approach, with Nelson rules[3] to help surface metrics which are starting to move out of control. I think it would have the advantage over ML techniques that it's easy to understand.

My experience is that alerting thresholds are a very poor mechanism for managing systems. They just ossify past disasters and typically become noise. Alert fatigue renders them meaningless. If they're set by the manufacturer then the incentives are broken, they will favour false alerts in order to push legal responsibility onto the operator.

[0] https://github.com/Netflix/Surus

[1] https://github.com/yzhao062/anomaly-detection-resources

[2] https://otexts.com/fpp2/

[3] https://en.wikipedia.org/wiki/Nelson_rules

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#34

Post by ares2012 »

zhangwins wrote: post #21
slap_shot wrote: post #17

Congrats on the launch. This is a really interesting space that I think has a ton of potential - I'm watching pretty closely to see what comes out of it. Have you heard of Outlier ( https://outlier.ai )? Do you have any thoughts? How does Orbiter compare to Outlier? (I haven't used Outlier but see it come up in anomaly detection discussion a lot recently).

Thank you! There's definitely a lot of growth and potential in this space and we're really excited too. We're focused on intelligent monitoring and alerting for metrics that the user cares about & defines. We also automate the diagnostic playbooks that teams use today after detecting an issue (eg check data, check user segments, check geographies, etc.) Outlier seems to focus on insights and less on monitoring/alerti…

Hi! (Founder of Outlier.ai here) You are right, our platform is designed to produce the most important insights from massive amounts of data, without requiring human supervision/configuration. It is most useful in applications when there is too much data to set up guardrails, or the teams don't know what guardrails to create. Our typical customers are very large consumer businesses who have data spread across dozens of systems and need to ensure they never miss important emerging trends or problems.

We are not an alerting or monitoring system, so I don't think you'd use us for the same applications as Orbital. The typical users of Outlier are the business users ranging from executives to business operations who want to make sure they are asking the right questions about the business.

Orbital looks like a great product, good luck in building your business!

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#35

Post by tixocloud »

zhangwins wrote: post #25
tixocloud wrote: post #24

Earlier quoted context omitted.

Funny, Actually I know a startup in Edinburgh that has figured out the “true organic traffic” and they’ve used ML to fix the data for marketing attribution model.

Was this ML attribution model output explainable / deterministic? I've seen some really complicated marketing attribution models in the past and hear it was something of a never-ending battle to understand and arrive at the "right" model.

I believe it is explainable as I didn’t hear anything fancy about the model being built. It’s been tested and proven to cut marketing spend quite a bit while delivering the same results. A patent has also been filed.

You are spot on that sometimes we just overcomplicate models and sometimes it’s best to go with something explainable and deterministic but less accurate as opposed to more accuracy but complicated.

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#36

Post by knightelvis »

zhangwins wrote: post #31
knightelvis wrote: post #30

Without looking into details of your soluton, what's the difference between your solution and Cloudwatch anomaly detection? https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitori...

Orbiter anomaly detection is for any DB (e.g. Postgres, Snowflake) and metrics that business/product teams tend to track such as transaction conversion %, user growth, add item to basket %, etc. Amazon Cloudwatch anomaly detection is for AWS resources & apps, and covers infra metrics like resource utilization, app performance, ops health. In terms of the anomaly detection capabilities -- both are using similar machin…

Thanks for replying. That's a good demo. However, I don't necessarily agree Cloudwatch is only for infra metrics. Theoretically, you could send any metrics to CW and leverage the anomaly detection feature. Given it aggregates data over time and you could lost granularities of your data, that's probably not a good idea for business centric data. Then I found AWS QuickSight (https://aws.amazon.com/quicksight/features-ml/?nc=sn&loc=2&d...) which seems to have a similar feature parity?

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#37

Post by longtermd »

I really like the manual configurability. In our startup, we work a lot with influencers and it's very usual for us to have strong spikes in signups (and also high/ low CVR for different quality of influencers and "strength of promotion"). This nature would a purely ML model to constantly shout alert.

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#38

Post by hohloma »

generatorguy wrote: post #10

I work on power stations which normally have about 1000 monitored variables per turbine-generator and another 500 for the plant in general. So typically 2500 for a two unit plant. Alarms are generated if a variable exceeds a threshold, or a binary variable is in the wrong state. Is Orbiter something that would benefit power plants?

There is a small company in Lund, sweden that specialized in this. Its run by a former professor of mine in uni. The basic idea is to build a model of the system and connect detectors output to it, and it will use that info to detect anomalies and filter errors to find root cause. https://www.goalart.com/ not affiliated in any way, except in already stated.

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#39

Post by rixed »

generatorguy wrote: post #10

I work on power stations which normally have about 1000 monitored variables per turbine-generator and another 500 for the plant in general. So typically 2500 for a two unit plant. Alarms are generated if a variable exceeds a threshold, or a binary variable is in the wrong state. Is Orbiter something that would benefit power plants?

Out of curiosity, since I'm interested in industrial monitoring: would you mind telling a bit more about the monitoring infrastructure, esp. how often are those metrics collected and what data protocols are involved in the process?

Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers

#40

Post by generatorguy »

jacques_chester wrote: post #33
generatorguy wrote: post #10

I work on power stations which normally have about 1000 monitored variables per turbine-generator and another 500 for the plant in general. So typically 2500 for a two unit plant. Alarms are generated if a variable exceeds a threshold, or a binary variable is in the wrong state. Is Orbiter something that would benefit power plants?

I think some sort of anomaly detection would be useful in your case. There are a bunch of libraries floating about, I remember at least Netflix[1], Yelp and Datadog talking about them. There appears to be a really good links page available too[1]. You can also learn a lot from Forecasting Principles and Practice , which is free online[2] I have previously pitched using a kind of SPC-for-metrics approach, with Nelson…

thanks for the links.

We only create an alert if there is a problem the operator can solve, otherwise there is no point in waking them up at 3 AM, so if anything our thresholds are set as loose as possible instead of as tight as possible.

However there are many instances where the operator could be alerted earlier that the machine operation is abnormal. For example the stator windings are rated for operation up to 155 degrees C but the machine is lightly loaded for a long time, the ambient temperature is normal, and the windings are 140 degrees. No alert would be generated from the stator winding temperature but something is amiss.

I think this is the case where some ML/AI/hypeword techniques might be applicable, for the controller to know that based on half a dozen variables the expected value for other variables based on past operation.

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