Go Winston! So much better than trying to do this in Tableau
Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
11–20 of 43 posts
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#12This sounds really cool! I've wished for something like this many times. I am mostly attracted by the fact that it would be mostly automatic. I am hoping it lives up to the hype.
Signed up for the beta. All the best!
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#13Congrats on launching! Looks very helpful! As a data scientist, I found that a drop in metrics was just as often due to a data pipeline issue as it was an actual business problem. This unfortunately causes business users to lose trust in the metrics quickly. How do you plan to differentiate between those two root causes of metric changes?
Ah I can empathize with you here (as a former DS) -- we had incidents in the past that were data pipeline / instrumentation changes causing bad data which then caused metric drops (versus a real product issue, but they nonetheless caused a loss of confidence in data). We think there are a number of diagnostic features that could be helpful here (to be built!). Teams today run playbooks to root cause issues when metri…
That's really cool! Besides identifying abrupt changes in metric X, for me the most difficult part is trying to understand what caused this change in X. Great to know that you have this issue in the roadmap, but do you think it's possible to develop a model/automation that is generic enough to be used in different business ?
Maybe analysing the correlation between different time series could be a way to go ?
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#14I 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?
Not OP, but I researched scalable anomaly detection systems for power-generating assets. We collaborated with a large industrial engine manufacturer on this work. https://arxiv.org/abs/1701.07500. The key challenge customers encountered was the prevalence of false alarms that led to unnecessary service.
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#15I 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?
Hey generatorguy - this is a really interesting use case so thanks for sharing. I imagine our modeling / monitoring / alerting capabilities can extend to power plants but will need to understand the data better. The common types of business and product metrics that our customers look for include user growth, cancellation rates, call failure %s, all of the above by different geos, etc. Happy to chat more if you'd like to shoot me an email (I'm winston[at]getorbiter.com)
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#16I 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?
Not OP, but I researched scalable anomaly detection systems for power-generating assets. We collaborated with a large industrial engine manufacturer on this work. https://arxiv.org/abs/1701.07500 . The key challenge customers encountered was the prevalence of false alarms that led to unnecessary service.
Woah this is awesome. How did you guys resolve the false alarm issue wrt power plants?
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#17Congrats 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).
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#18Congrats on the launch. We’re in an adjacent/overlapping space with drift detection/model monitoring so it’s always very exciting to see automatic data monitoring tools come into place. We’re hoping that as more and more startups come onboard the better it is for all of us. Cheers and best wishes!
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#19Earlier quoted context omitted.
Ah I can empathize with you here (as a former DS) -- we had incidents in the past that were data pipeline / instrumentation changes causing bad data which then caused metric drops (versus a real product issue, but they nonetheless caused a loss of confidence in data). We think there are a number of diagnostic features that could be helpful here (to be built!). Teams today run playbooks to root cause issues when metri…
That's really cool! Besides identifying abrupt changes in metric X, for me the most difficult part is trying to understand what caused this change in X. Great to know that you have this issue in the roadmap, but do you think it's possible to develop a model/automation that is generic enough to be used in different business ? Maybe analysing the correlation between different time series could be a way to go ?
It’s definitely possible if you have the underlying data definitions so you’re not having to compare time-series across industries (it’ll be hard because every single business’ metrics could be so different based on the way the metrics themselves are setup).
Avora (https://avora.com/product/) and Thoughtspot (https://Thoughtspot.com) all have the root cause capability
Re: Launch HN: Orbiter (YC W20) – Autonomous data monitoring for non-engineers
#20Congrats on the launch. We’re in an adjacent/overlapping space with drift detection/model monitoring so it’s always very exciting to see automatic data monitoring tools come into place. We’re hoping that as more and more startups come onboard the better it is for all of us. Cheers and best wishes!
Thank you! We're actually a team of Canadians too (but have been living/working in SF Bay Area) :D Always great to see more applications for data science - best of wishes to you too!