6 ms·

Statistical Process Control in Python

230 points by lifeisstillgood 11mo ago

srean 11mo ago
Fun memories. We have successfully replaced thousands of complicated deep net time series based anomaly detectors at a FANG with statistical (nonparametric, semiparametric) process control ones. They use 3 to 4 orders lower number of trained parameters and have just enough complexity that a team of 3 or four can handle several thousands of such streams. The amount of baby sitting that deep net models needed was astronomical, debugging and understanding what has happened quite opaque. For small teams, with limited resources I would still heavily recommend stats based models for time series anomaly detection. May not be your best career move right now for political reasons. Those making massive bets do not like to confront that some of their bets might not have been well placed. They may try to make it difficult for contrary evidence to become too visible.
- auxiliarymoose 11mo ago
  Super cool, thanks for sharing! This is one of the reasons I am so skeptical of the current AI hype cycle. There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today. You'd think that rational businesses would take the low-risk snooze-fest high-margin option any day instead of unintelligible and unreliable options that demand a lot of resources, and yet...
  - menaerus 11mo ago
    > There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today. I know some examples but not too many. Care to share more examples?
    - auxiliarymoose 11mo ago
      Some off the top of my head... - Instead of trying to get LLMs to answer user questions, write better FAQs informed by reviewing tickets submitted by customers - Instead of RAG for anything involving business data, have some DBA write a bunch of reports that answer specific business questions - Instead of putting some copilot chat into tools and telling users to ask it to e.g. "explain recent sales trends", make task-focused wizards and visualizations so users can answer these with hard numbers - Instead of generating code with LLMs, write more expressive frameworks and libraries that don't require so much plumbing and boilerplate Of course, maybe there is something I am missing, but these are just my personal observations!
      - menaerus 11mo ago
        With all due respect, all of those examples are the examples of "yesterday" ... that's how we have been bringing money to businesses for decades, no? Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned. Businesses are incentivized to be more productive and cost-effective since they are solely profit-driven so they naturally see this as an opportunity to make more money by hiring less people while keeping the amount of work done roughly the same or even more. So "classical" approach to many of the problems is I think the thing of a past already.
        auxiliarymoose 11mo ago
        > Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned. We really don't. There are demos that look cool onstage, but there is a big difference between "in store good" and "at home good" in the sense that products aren't living up to their marketing during actual use. IMO there is a lot of room to grow within the traditional approaches of "yesterday" - The problem is that large orgs get bogged down in legacy + bureaucracy, and most startups don't understand the business problems well enough to make a better solution. And I don't think that there is any technical silver bullet that can solve either of these problems (AI or otherwise)
        menaerus 11mo ago
        I am wondering how often do you use AI models? Because I do it on a daily basis, and as much as they have limitations, I find them to be performing incredibly well. It's far very far from being a demo - last time it was a demo that looked "cool" was around 2020/21 when they were cool for spitting out the haiku poetry, and perhaps 2022 when capabilities were not as good. But today? Completely mind-blowing. If you're not convinced, I suggest you to search for the law firms, hospitals, and laboratories ... all of which are using AI models as of today to do both the research and boiler-plate work. Creative industries are being literally erased by the generative AI as we are speaking. What will happen with the Photoshop and other similar tools when I can create whatever I want using the free AI model in literally 2 seconds without prior knowledge? What will happen with majority of movie effect makers when single guy will be able to do the work of 5 people at the same time? Or interior designers? The heck, what will happen with the Google search - I anticipate nobody will be using it in a year or two. I already don't because it's a massive sink of time compared to what I can do with perplexity for example. There's many many examples. You just need to have your mind open to see it.
    - srean 11mo ago
      In the realm of data science, Linear models and SAT solvers used cleverly will get you a surprisingly long way.
      - menaerus 11mo ago
        I thought the OCR was one of the obvious examples where we have a classical technology that is already working very well but in the long-run I don't see it surviving. _Generic_ AI models already can do the OCR kinda good but they are not even trained for that purpose, it's almost incidental - they've never been trained to extract the, let's say name/surname from some sort of a document with a completely unfamiliar structure, but the crazy thing is that it does work somehow! I think that once somebody finetunes the AI model only for this purpose I think there's a good chance it will outperform classical approach in terms of precision and scalability.
        srean 11mo ago
        In general I agree. For OCR I agree vehemently. Part of the reason is the structure of the solution (convolutions) match the space so well. The failure cases are those where AI solutions have to stay in a continuous debug, train, update mode. Then you have to think about the resources you need, both in terms of people as well as compute to maintain such a solution. Because of the way the world works, it's endemic nonstationarity, the debug-retrain-update is a common state of affairs even in traditional stats and ML.
        menaerus 11mo ago
        I see. Let's take another example here, I hope I understood you - imagine you have an AI model which is connected to all of your company's in-house data generation sources such as wiki, chat, jira, emails, merge requests, excel sheets, etc. Basically everything that can be deemed useful to query or to create business inteligence on top of. These data sources are continously generating more and more data every day, and given their nature they are more or less unstructured. Yet, we have such systems in place where we don't have to retrain the model with ever-growing data. This is one example I could think of but it kinda suggests that models, at least for some purposes, don't have to be retrained continuously to keep them running well. I also use a technique of explaining something to the AI model he has not seen before (according to the wrong answer I got from it previously), and it manages to evolve the steps, whatever they are, so that it gives me the correct answer in the end. This also suggests that capacity of the models is larger than what they have been trained on.
    - AlotOfReading 11mo ago
      In my domain, I see lots of people reaching immediately for "AI" techniques to solve sensor fusion and state estimation problems where a traditional Kalman filter type solution would be faster and much more interpretable.
      - menaerus 11mo ago
        Incidentally, I worked on the exact same thing - Kalman filtering for tracking objects in hard real-time systems. And it is not quite as simple as one would think - developing mathematical models for all kinds of different objects that one might wanna track is far from trivial, and it was difficult to model the real-world with more or less simplistic discrete equations. And it didn't work completely reliably so we needed an extra layer of confidence - I don't remember what we used back then but it was yet another algorithm with yet another source of data.
        srean 11mo ago
        Sigma point filters ? The escalation ladder is usually KF, EKF, unscented KF, sigma point ...
        menaerus 11mo ago
        I believe it was random forest that we combined with EKF.
        AlotOfReading 11mo ago
        There's a lot of situations where you genuinely need more sophisticated algorithms. I'm talking about people reaching for those kinds of things before they determine the traditional algorithms won't work.
  - srean 11mo ago
    It does not work that way in the short term. Say you have bet billions as a CEO, CTO, CFO. The decision has already been made. Such a steep price had to come at the cost of many groups and teams and projects in the company. Now is not a time to water plants that offer alternatives. You will have a smoother ride choosing tools that justifies that billion dollar bet.
    - auxiliarymoose 11mo ago
      Decision-making in organizations is definitely a hard problem. I think an uncomfortable reality is that a lot of decisions (technology, strategy, etc.) are not optimal or even rational, but more just an outcome of personal preferences. Even data-driven approaches aren't immune since they depend on the analysis and interpretation of the data (which is subjective).
      - srean 11mo ago
        Data informed is good. Purely data driven is a bad idea. After all even in Physics big advances came from thought experiments. Data is one way to reason about a decision, logic and knowledgebase is another way. Both can be very powerful if one retains the humility of fallibility. In organizations one common failure mode is that the organisational level at which decisions are made are not the same levels where the decisions are going to have their effects felt. It's a really difficult problem to solve. Too much decentralisation is also a bad idea. You get the mess of unplanned congested cities.
        cb321 11mo ago
        For a while now, I've been summarizing the ease with which everything turns into a "Humanity Complete" problem via: "Delegation affords so much, but trust sure is tricky." This has been observed forever in various forms/contexts. Planning & policy people call them "Wicked Problems" (https://en.wikipedia.org/wiki/Wicked_problem https://en.wikipedia.org/wiki/Wicked_problem). The Philosophy of Science one goes by the Demarcation Problem (https://en.wikipedia.org/wiki/Demarcation_problem https://en.wikipedia.org/wiki/Demarcation_problem) { roughly, in the sense that the really hard nugget connects to "trust" }. At least one aspect of all of it is that trust is a little like money/capital and "faking it" is a bit like "stealing". The game theory of it is that since faking is virtually always vastly cheaper there are (eventually) huge incentives to do so, at some point by someone(s). So, almost any kind of trust/delegation structure has a strong pull toward "decay", from knock-off brands to whatever. It just takes a sadly small fraction of Prisoner's Dilemma defectors to ruin things/systems thereof. 2nd law of thermo makes order cost energy and this decay feels like almost an isomorphic (maybe even the same..?) thing. It's not just product/tech enshittification, but that might be yet another special case/example. Anyway, I have no great answers or as some responder to me a while back said, if I did, I'd "have a Nobel and possibly be the first president of the united planet".
  - DavidPeiffer 11mo ago
    >This is one of the reasons I am so skeptical of the current AI hype cycle. There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today. In 2013 my statistics professor warned that once we are in the real world, "people will come up to you trying to sell fancy machine learning models for big money, though the simple truth is that many problems can be solved better by applying straightforward statistical methods". There has always been the ML hype, but the last couple years are a whole different level.
  - fuzzfactor 11mo ago
    >unintelligible and unreliable options that demand a lot of resources Some options have more persuasive salesmen than others.
- kqr 11mo ago
  What confuses me about deep nets is that there's rarely enough signal to be able to meaningfully train a large number of parameters. Surely 99 % of those parameters are either (a) incredibly unstable, or (b) correlate perfectly with other parameters?
  - srean 11mo ago
    They do. There are enormous redundancies. There's a manifold over which the parameters can vary wildly yet do zilch to the output. The nonlinear analogue of a null space. Parameter instability does not worry a machine learner as much as it worries a statistician. ML folks worry about output instabilities. The current understanding goes that this overparameterization makes reaching good configurations easier while keeping the search algorithm as simple as stochastic gradient descent.
    - kqr 11mo ago
      Huh, I didn't know that! Are there efforts to automatically reduce the number of parameters once the model is trained? Or do the relationships between parameters end up too complicated to do that? I would assume such a reduction would be useful for explainability. (Asking specifically about time series models and such.)
      - srean 11mo ago
        What you are looking for is the lottery ticket hypothesis for neural networks. Hit a search engine with those words you will find examples. https://arxiv.org/abs/1803.03635 https://arxiv.org/abs/1803.03635 ( you can follow up on semantic scholar for more) Selecting which weights to discard seems as hard as the original problem. But random decimation, sometimes barely informed decimation have been observed to be effective. On the theory side now it's understood that in the thicket of weights, lurk a much much smaller subset that can have nearly the same output. These observations are for DNNs in general. For time series specifically I don't know what the state of the art is. In general NNs are still catching up with traditional stats approaches in this domain. There are a few examples where traditional approaches have been beaten, but only a few. One good source to watch are the M series of competitions.
- oulipo2 11mo ago
  could you give a brief overview of: - what libs were you using - what kind of algos / models were most useful for what kind of data? I have an IoT use-case, I wanted to look both at NNs and more classical stats models to see if it has value
  - srean 11mo ago
    Can't for obvious reasons. But no specialized libraries used. The usual Python stack that comes packaged for any respectable OS distribution these days, mixed in with other close-to-the-metal languages for performance or API compatibility reasons. Look up nonparametric statistical process control and you will find useful papers. The algorithms are actually quite simple to implement. If the algorithms are not simple then probably they are not worth your time. The analysis in the paper might be complicated, don't worry about that, look for simplicity of the algorithms.
  - paddleon 11mo ago
    did similar work at similar scale to srean. Assume you have signal from one IoT device, say a sensor reading. Anomalies are sudden changes in the value of the signal. Define sudden (using the time delta between observations and your other domain knowledge); let's say the sensor reports 1x/second and sudden means 1-3 minutes. Simple options: rolling mean last 3 values/rolling mean last 60 values. If this value is over a threshold, alert Say the readings are normally distributed, or they can be detrended/made normal via a simple 1 or 2 stage AR/MA model. Apply the https://en.wikipedia.org/wiki/Western_Electric_rules https://en.wikipedia.org/wiki/Western_Electric_rules to detect anomalies. Complexer but still simple options. Say you have IoT sensors over a larger area, and an anomaly is one sensor which is higher than others. Run roughly the same analysis as above, but on the correlation matrix of all the sensors. Look for rapidly changing correlations. example: temperature detectors in each room of your house, and your kid opens the front door to go play in the snow. The entry hall cools down while the rest of the house's temp stays roughly stable. You can picture what that does to the correlation matrix.
    - srean 11mo ago
      Bang on. It was little more complicated to remove temporal dependencies from the original streams and we could not rely on Gaussian behaviour. Other than that, it's pretty much the same, barring an effort to keep the alerting thresholds unaffected by recent anomalies.
- traceroute66 11mo ago
  > We have successfully replaced thousands of complicated deep net time series based anomaly detectors at a FANG with statistical (nonparametric, semiparametric) process control ones. Interesting. Were you using things like Matrix Profile too ? And if so, have those been replaced too ?
  - srean 11mo ago
    In this specific project no, but in others a very emphatic yes.
  - eitally 11mo ago
    Fwiw, I have a masters in operations research as a focus area within an industrial engineering degree, and spent 15 years working in manufacturing systems with a focus on test automation & quality. Traditional SPC/SQC analysis is, and will remain, king -- at least for some time. That can potentially evolve on high-vol/low-mix scenarios that lend themselves more easily to training models on anomaly detection, but especially for complex product manufacturing in high-mix factories that's not the case. It's far better to let your test/quality engineers do their jobs and figure out statistical controls on their own. Among other reasons, this is largely true because acceptable ranges for different anomaly & defect types can vary significantly for different revs of a single product, or even sub-revs (things that are tied to an ECO but don't result in incrementing the product rev), or -- more crucially -- the line the product is manufactured on. One thing that's notoriously tricky to troubleshoot without being physically onsite is whether a defect is because of a machine, because of a person, or because of faulty piece parts/material. Understanding and knowing how to apply traditional statistical analysis to these problems -- and also designing useful data structures to store all the data you're collecting -- is far more valuable right now than trying to shoehorn in an AI model to do this work.
- esafak 11mo ago
  Can you be more specific about what SPC algorithm you moved to? Did you trade off prediction quality for complexity, increasing the number of false alarms?
  - srean 11mo ago
    We generally targeted specific statistics of derived/processed streams. For some such streams we cared if the mean changed. In others if the spread changed in a way that was unusual for the time of day. In yet others if some percentile changed that was unusual for the time of day. Sometimes it will be more than one of such statistics. Then we would track an online estimator of that measure with an SPC chart. The thresholds would be set based on our appetite for false alarms. We did not fit or use properties of parametric distributions that standard SPC charts use. So no 3-sigma business. In our case convergence to Gaussian would often be not fast enough for such techniques to be useful. Also the original streams were far from IID, temporal dependencies were strong. So we had to derive from them derived streams that didn't show temporal dependencies any longer, at least not as strongly. This was the most important bit. The next key aspect was to keep the alerting thresholds as untarnished and unaffected as possible from the outliers that would unavoidably occur. Getting this to work without additional human supervisory labels was the next most important part. Make this part too robust to outliers then the system would not automatically adapt to a new normal. Make it too sensitive and we would get overwhelmed by false positives.
    - quasse 11mo ago
      > So we had to derive from them derived streams that didn't show temporal dependencies any longer, at least not as strongly Could you expand on that (or at least point me towards some keywords to read up on)? I think I understand conceptually what this means (network latency increases at noon CDT each day because YouTube load increases during the lunch hour, as an example) but I'm wondering how you normalize data streams for temporal dependencies with unknown frequencies (nominal change #1 happens each Sunday, while nominal change #2 happens each day at noon).
      - 11mo ago
- wikibob 11mo ago
  This sounds fascinating. Can you say anything about the application? Autoscaling? Data center cooling and power use?
  - srean 11mo ago
    Would rather not. Just to be in the compliant zone legally and also to stay somewhat anonymous. Sincerely sorry to disappoint. But let me assure you it was nothing exotic.
    - wikibob 11mo ago
      Fair. Not sure what it’s like getting tech talks approved through comms these days, but this would be fascinating to hear about at a SF or SouthBay Systems meetup.
- vincenthwt 11mo ago
  Thank you for your perspective. As a machine vision engineer in the semiconductor industry, I have seen a lot of hype around deep learning and AI for vision applications. From my experience, deep learning works well for OCR but less so for classification tasks. I often achieve better results by focusing on good lighting and using classical computer vision techniques. I agree with your point about the politics of technology adoption. To protect my career, I usually promote hybrid approaches that combine deep learning and traditional computer vision methods. In reality, many deep learning solutions still rely heavily on classical techniques. Your comments on political challenges and decision-making in technology are very relevant to my experience.
  - srean 11mo ago
    Yes hybrid and ensembles would be a good way to handle this.
- collinmcnulty 11mo ago
  My first big career move was similar. Employer had all these complicated ML models that played great at conferences, but operators who had to use them said they were inaccurate and indecipherable. I asked the operators what they used to sort real vs false alarms, and the answers were very simple mathematical relationships. Ripped out the ML, coded up the operators versions with some slightly tuned up statistics and got dramatically better results.
tamagotchiguy 11mo ago
On a sidenote, love the look and feel of your page!!
kqr 11mo ago
For beginners to SPC, I wrote a practitioner's guide a couple of years ago. Might provide meaningful context: https://entropicthoughts.com/statistical-process-control-a-practitioners-guide https://entropicthoughts.com/statistical-process-control-a-p...
kasperset 11mo ago
Classical stats is still bread and butter for lot of smallish dataset in clinical datasets. It is hard to do machine learning or even regression on some very preliminary data. Metadata is tough to collect and harmonize so it becomes hard to integrate specially with human studies with rare diseases.
world2vec 11mo ago
I'm old enough that I got my Lean Six Sigma Green Belt certification and used Minitab (!!!) to do a statistical process control project on some investment banking back office process that was being automated. Does anyone here even remember Minitab? You kids and your newfangled Python!
- srean 11mo ago
  My uni had its manual in its library is all I can say :)
- 8x 11mo ago
  I got a six sigma green belt using minitab in ~2018. It's still in use and getting updates, for example they now provide an interface to python: https://pypi.org/project/mtbpy/ https://pypi.org/project/mtbpy/
- bmez 11mo ago
  Minitab had a good SPC toolbox back in the day. For the longest time, open source solutions were incomplete in the sense that all of them did x-bar/S/R and then usually never got to the more esoteric but handy stuff. Multivariate, even less support.
- nxobject 11mo ago
  I had the privilege of working at a JMP shop! It still gives me the warm and fuzzies.
hbarka 11mo ago
In the real world, data is never this clean. Majority of the time is data quality work because you will see outliers that might be due to measurement error, calibration, process changes. It requires familiarity with the process and having an understanding and intuition for why the shape of the data distribution in a process is the way it is. Because ad-hoc data visualization and exploration is critical here, enterprise requirements need mature tools that can be used quickly. BYOT and DIY code for an SPC Cpk chart is not what you want to be doing.
runawayprocess 11mo ago
SPC is so great! Far simpler than many other toolsets that don’t work well.
drumnerd 11mo ago
Many type errors

230 points by lifeisstillgood 11mo ago

srean 11mo ago

Fun memories. We have successfully replaced thousands of complicated deep net time series based anomaly detectors at a FANG with statistical (nonparametric, semiparametric) process control ones. They use 3 to 4 orders lower number of trained parameters and have just enough complexity that a team of 3 or four can handle several thousands of such streams. The amount of baby sitting that deep net models needed was astronomical, debugging and understanding what has happened quite opaque. For small teams, with limited resources I would still heavily recommend stats based models for time series anomaly detection. May not be your best career move right now for political reasons. Those making massive bets do not like to confront that some of their bets might not have been well placed. They may try to make it difficult for contrary evidence to become too visible.

auxiliarymoose 11mo ago
Super cool, thanks for sharing! This is one of the reasons I am so skeptical of the current AI hype cycle. There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today. You'd think that rational businesses would take the low-risk snooze-fest high-margin option any day instead of unintelligible and unreliable options that demand a lot of resources, and yet...
- menaerus 11mo ago
  > There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today. I know some examples but not too many. Care to share more examples?
  - auxiliarymoose 11mo ago
    Some off the top of my head... - Instead of trying to get LLMs to answer user questions, write better FAQs informed by reviewing tickets submitted by customers - Instead of RAG for anything involving business data, have some DBA write a bunch of reports that answer specific business questions - Instead of putting some copilot chat into tools and telling users to ask it to e.g. "explain recent sales trends", make task-focused wizards and visualizations so users can answer these with hard numbers - Instead of generating code with LLMs, write more expressive frameworks and libraries that don't require so much plumbing and boilerplate Of course, maybe there is something I am missing, but these are just my personal observations!
    - menaerus 11mo ago
      With all due respect, all of those examples are the examples of "yesterday" ... that's how we have been bringing money to businesses for decades, no? Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned. Businesses are incentivized to be more productive and cost-effective since they are solely profit-driven so they naturally see this as an opportunity to make more money by hiring less people while keeping the amount of work done roughly the same or even more. So "classical" approach to many of the problems is I think the thing of a past already.
      - auxiliarymoose 11mo ago
        > Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned. We really don't. There are demos that look cool onstage, but there is a big difference between "in store good" and "at home good" in the sense that products aren't living up to their marketing during actual use. IMO there is a lot of room to grow within the traditional approaches of "yesterday" - The problem is that large orgs get bogged down in legacy + bureaucracy, and most startups don't understand the business problems well enough to make a better solution. And I don't think that there is any technical silver bullet that can solve either of these problems (AI or otherwise)
        menaerus 11mo ago
        I am wondering how often do you use AI models? Because I do it on a daily basis, and as much as they have limitations, I find them to be performing incredibly well. It's far very far from being a demo - last time it was a demo that looked "cool" was around 2020/21 when they were cool for spitting out the haiku poetry, and perhaps 2022 when capabilities were not as good. But today? Completely mind-blowing. If you're not convinced, I suggest you to search for the law firms, hospitals, and laboratories ... all of which are using AI models as of today to do both the research and boiler-plate work. Creative industries are being literally erased by the generative AI as we are speaking. What will happen with the Photoshop and other similar tools when I can create whatever I want using the free AI model in literally 2 seconds without prior knowledge? What will happen with majority of movie effect makers when single guy will be able to do the work of 5 people at the same time? Or interior designers? The heck, what will happen with the Google search - I anticipate nobody will be using it in a year or two. I already don't because it's a massive sink of time compared to what I can do with perplexity for example. There's many many examples. You just need to have your mind open to see it.
  - srean 11mo ago
    In the realm of data science, Linear models and SAT solvers used cleverly will get you a surprisingly long way.
    - menaerus 11mo ago
      I thought the OCR was one of the obvious examples where we have a classical technology that is already working very well but in the long-run I don't see it surviving. _Generic_ AI models already can do the OCR kinda good but they are not even trained for that purpose, it's almost incidental - they've never been trained to extract the, let's say name/surname from some sort of a document with a completely unfamiliar structure, but the crazy thing is that it does work somehow! I think that once somebody finetunes the AI model only for this purpose I think there's a good chance it will outperform classical approach in terms of precision and scalability.
      - srean 11mo ago
        In general I agree. For OCR I agree vehemently. Part of the reason is the structure of the solution (convolutions) match the space so well. The failure cases are those where AI solutions have to stay in a continuous debug, train, update mode. Then you have to think about the resources you need, both in terms of people as well as compute to maintain such a solution. Because of the way the world works, it's endemic nonstationarity, the debug-retrain-update is a common state of affairs even in traditional stats and ML.
        menaerus 11mo ago
        I see. Let's take another example here, I hope I understood you - imagine you have an AI model which is connected to all of your company's in-house data generation sources such as wiki, chat, jira, emails, merge requests, excel sheets, etc. Basically everything that can be deemed useful to query or to create business inteligence on top of. These data sources are continously generating more and more data every day, and given their nature they are more or less unstructured. Yet, we have such systems in place where we don't have to retrain the model with ever-growing data. This is one example I could think of but it kinda suggests that models, at least for some purposes, don't have to be retrained continuously to keep them running well. I also use a technique of explaining something to the AI model he has not seen before (according to the wrong answer I got from it previously), and it manages to evolve the steps, whatever they are, so that it gives me the correct answer in the end. This also suggests that capacity of the models is larger than what they have been trained on.
  - AlotOfReading 11mo ago
    In my domain, I see lots of people reaching immediately for "AI" techniques to solve sensor fusion and state estimation problems where a traditional Kalman filter type solution would be faster and much more interpretable.
    - menaerus 11mo ago
      Incidentally, I worked on the exact same thing - Kalman filtering for tracking objects in hard real-time systems. And it is not quite as simple as one would think - developing mathematical models for all kinds of different objects that one might wanna track is far from trivial, and it was difficult to model the real-world with more or less simplistic discrete equations. And it didn't work completely reliably so we needed an extra layer of confidence - I don't remember what we used back then but it was yet another algorithm with yet another source of data.
      - srean 11mo ago
        Sigma point filters ? The escalation ladder is usually KF, EKF, unscented KF, sigma point ...
        menaerus 11mo ago
        I believe it was random forest that we combined with EKF.
      - AlotOfReading 11mo ago
        There's a lot of situations where you genuinely need more sophisticated algorithms. I'm talking about people reaching for those kinds of things before they determine the traditional algorithms won't work.
- srean 11mo ago
  It does not work that way in the short term. Say you have bet billions as a CEO, CTO, CFO. The decision has already been made. Such a steep price had to come at the cost of many groups and teams and projects in the company. Now is not a time to water plants that offer alternatives. You will have a smoother ride choosing tools that justifies that billion dollar bet.
  - auxiliarymoose 11mo ago
    Decision-making in organizations is definitely a hard problem. I think an uncomfortable reality is that a lot of decisions (technology, strategy, etc.) are not optimal or even rational, but more just an outcome of personal preferences. Even data-driven approaches aren't immune since they depend on the analysis and interpretation of the data (which is subjective).
    - srean 11mo ago
      Data informed is good. Purely data driven is a bad idea. After all even in Physics big advances came from thought experiments. Data is one way to reason about a decision, logic and knowledgebase is another way. Both can be very powerful if one retains the humility of fallibility. In organizations one common failure mode is that the organisational level at which decisions are made are not the same levels where the decisions are going to have their effects felt. It's a really difficult problem to solve. Too much decentralisation is also a bad idea. You get the mess of unplanned congested cities.
      - cb321 11mo ago
        For a while now, I've been summarizing the ease with which everything turns into a "Humanity Complete" problem via: "Delegation affords so much, but trust sure is tricky." This has been observed forever in various forms/contexts. Planning & policy people call them "Wicked Problems" (https://en.wikipedia.org/wiki/Wicked_problem https://en.wikipedia.org/wiki/Wicked_problem). The Philosophy of Science one goes by the Demarcation Problem (https://en.wikipedia.org/wiki/Demarcation_problem https://en.wikipedia.org/wiki/Demarcation_problem) { roughly, in the sense that the really hard nugget connects to "trust" }. At least one aspect of all of it is that trust is a little like money/capital and "faking it" is a bit like "stealing". The game theory of it is that since faking is virtually always vastly cheaper there are (eventually) huge incentives to do so, at some point by someone(s). So, almost any kind of trust/delegation structure has a strong pull toward "decay", from knock-off brands to whatever. It just takes a sadly small fraction of Prisoner's Dilemma defectors to ruin things/systems thereof. 2nd law of thermo makes order cost energy and this decay feels like almost an isomorphic (maybe even the same..?) thing. It's not just product/tech enshittification, but that might be yet another special case/example. Anyway, I have no great answers or as some responder to me a while back said, if I did, I'd "have a Nobel and possibly be the first president of the united planet".
- DavidPeiffer 11mo ago
  >This is one of the reasons I am so skeptical of the current AI hype cycle. There are boring, well-behaved classical solutions for many of the use-cases where fancy ML is pushed today. In 2013 my statistics professor warned that once we are in the real world, "people will come up to you trying to sell fancy machine learning models for big money, though the simple truth is that many problems can be solved better by applying straightforward statistical methods". There has always been the ML hype, but the last couple years are a whole different level.
- fuzzfactor 11mo ago
  >unintelligible and unreliable options that demand a lot of resources Some options have more persuasive salesmen than others.
kqr 11mo ago
What confuses me about deep nets is that there's rarely enough signal to be able to meaningfully train a large number of parameters. Surely 99 % of those parameters are either (a) incredibly unstable, or (b) correlate perfectly with other parameters?
- srean 11mo ago
  They do. There are enormous redundancies. There's a manifold over which the parameters can vary wildly yet do zilch to the output. The nonlinear analogue of a null space. Parameter instability does not worry a machine learner as much as it worries a statistician. ML folks worry about output instabilities. The current understanding goes that this overparameterization makes reaching good configurations easier while keeping the search algorithm as simple as stochastic gradient descent.
  - kqr 11mo ago
    Huh, I didn't know that! Are there efforts to automatically reduce the number of parameters once the model is trained? Or do the relationships between parameters end up too complicated to do that? I would assume such a reduction would be useful for explainability. (Asking specifically about time series models and such.)
    - srean 11mo ago
      What you are looking for is the lottery ticket hypothesis for neural networks. Hit a search engine with those words you will find examples. https://arxiv.org/abs/1803.03635 https://arxiv.org/abs/1803.03635 ( you can follow up on semantic scholar for more) Selecting which weights to discard seems as hard as the original problem. But random decimation, sometimes barely informed decimation have been observed to be effective. On the theory side now it's understood that in the thicket of weights, lurk a much much smaller subset that can have nearly the same output. These observations are for DNNs in general. For time series specifically I don't know what the state of the art is. In general NNs are still catching up with traditional stats approaches in this domain. There are a few examples where traditional approaches have been beaten, but only a few. One good source to watch are the M series of competitions.
oulipo2 11mo ago
could you give a brief overview of: - what libs were you using - what kind of algos / models were most useful for what kind of data? I have an IoT use-case, I wanted to look both at NNs and more classical stats models to see if it has value
- srean 11mo ago
  Can't for obvious reasons. But no specialized libraries used. The usual Python stack that comes packaged for any respectable OS distribution these days, mixed in with other close-to-the-metal languages for performance or API compatibility reasons. Look up nonparametric statistical process control and you will find useful papers. The algorithms are actually quite simple to implement. If the algorithms are not simple then probably they are not worth your time. The analysis in the paper might be complicated, don't worry about that, look for simplicity of the algorithms.
- paddleon 11mo ago
  did similar work at similar scale to srean. Assume you have signal from one IoT device, say a sensor reading. Anomalies are sudden changes in the value of the signal. Define sudden (using the time delta between observations and your other domain knowledge); let's say the sensor reports 1x/second and sudden means 1-3 minutes. Simple options: rolling mean last 3 values/rolling mean last 60 values. If this value is over a threshold, alert Say the readings are normally distributed, or they can be detrended/made normal via a simple 1 or 2 stage AR/MA model. Apply the https://en.wikipedia.org/wiki/Western_Electric_rules https://en.wikipedia.org/wiki/Western_Electric_rules to detect anomalies. Complexer but still simple options. Say you have IoT sensors over a larger area, and an anomaly is one sensor which is higher than others. Run roughly the same analysis as above, but on the correlation matrix of all the sensors. Look for rapidly changing correlations. example: temperature detectors in each room of your house, and your kid opens the front door to go play in the snow. The entry hall cools down while the rest of the house's temp stays roughly stable. You can picture what that does to the correlation matrix.
  - srean 11mo ago
    Bang on. It was little more complicated to remove temporal dependencies from the original streams and we could not rely on Gaussian behaviour. Other than that, it's pretty much the same, barring an effort to keep the alerting thresholds unaffected by recent anomalies.
traceroute66 11mo ago
> We have successfully replaced thousands of complicated deep net time series based anomaly detectors at a FANG with statistical (nonparametric, semiparametric) process control ones. Interesting. Were you using things like Matrix Profile too ? And if so, have those been replaced too ?
- srean 11mo ago
  In this specific project no, but in others a very emphatic yes.
- eitally 11mo ago
  Fwiw, I have a masters in operations research as a focus area within an industrial engineering degree, and spent 15 years working in manufacturing systems with a focus on test automation & quality. Traditional SPC/SQC analysis is, and will remain, king -- at least for some time. That can potentially evolve on high-vol/low-mix scenarios that lend themselves more easily to training models on anomaly detection, but especially for complex product manufacturing in high-mix factories that's not the case. It's far better to let your test/quality engineers do their jobs and figure out statistical controls on their own. Among other reasons, this is largely true because acceptable ranges for different anomaly & defect types can vary significantly for different revs of a single product, or even sub-revs (things that are tied to an ECO but don't result in incrementing the product rev), or -- more crucially -- the line the product is manufactured on. One thing that's notoriously tricky to troubleshoot without being physically onsite is whether a defect is because of a machine, because of a person, or because of faulty piece parts/material. Understanding and knowing how to apply traditional statistical analysis to these problems -- and also designing useful data structures to store all the data you're collecting -- is far more valuable right now than trying to shoehorn in an AI model to do this work.
esafak 11mo ago
Can you be more specific about what SPC algorithm you moved to? Did you trade off prediction quality for complexity, increasing the number of false alarms?
- srean 11mo ago
  We generally targeted specific statistics of derived/processed streams. For some such streams we cared if the mean changed. In others if the spread changed in a way that was unusual for the time of day. In yet others if some percentile changed that was unusual for the time of day. Sometimes it will be more than one of such statistics. Then we would track an online estimator of that measure with an SPC chart. The thresholds would be set based on our appetite for false alarms. We did not fit or use properties of parametric distributions that standard SPC charts use. So no 3-sigma business. In our case convergence to Gaussian would often be not fast enough for such techniques to be useful. Also the original streams were far from IID, temporal dependencies were strong. So we had to derive from them derived streams that didn't show temporal dependencies any longer, at least not as strongly. This was the most important bit. The next key aspect was to keep the alerting thresholds as untarnished and unaffected as possible from the outliers that would unavoidably occur. Getting this to work without additional human supervisory labels was the next most important part. Make this part too robust to outliers then the system would not automatically adapt to a new normal. Make it too sensitive and we would get overwhelmed by false positives.
  - quasse 11mo ago
    > So we had to derive from them derived streams that didn't show temporal dependencies any longer, at least not as strongly Could you expand on that (or at least point me towards some keywords to read up on)? I think I understand conceptually what this means (network latency increases at noon CDT each day because YouTube load increases during the lunch hour, as an example) but I'm wondering how you normalize data streams for temporal dependencies with unknown frequencies (nominal change #1 happens each Sunday, while nominal change #2 happens each day at noon).
    - 11mo ago
wikibob 11mo ago
This sounds fascinating. Can you say anything about the application? Autoscaling? Data center cooling and power use?
- srean 11mo ago
  Would rather not. Just to be in the compliant zone legally and also to stay somewhat anonymous. Sincerely sorry to disappoint. But let me assure you it was nothing exotic.
  - wikibob 11mo ago
    Fair. Not sure what it’s like getting tech talks approved through comms these days, but this would be fascinating to hear about at a SF or SouthBay Systems meetup.
vincenthwt 11mo ago
Thank you for your perspective. As a machine vision engineer in the semiconductor industry, I have seen a lot of hype around deep learning and AI for vision applications. From my experience, deep learning works well for OCR but less so for classification tasks. I often achieve better results by focusing on good lighting and using classical computer vision techniques. I agree with your point about the politics of technology adoption. To protect my career, I usually promote hybrid approaches that combine deep learning and traditional computer vision methods. In reality, many deep learning solutions still rely heavily on classical techniques. Your comments on political challenges and decision-making in technology are very relevant to my experience.
- srean 11mo ago
  Yes hybrid and ensembles would be a good way to handle this.
collinmcnulty 11mo ago
My first big career move was similar. Employer had all these complicated ML models that played great at conferences, but operators who had to use them said they were inaccurate and indecipherable. I asked the operators what they used to sort real vs false alarms, and the answers were very simple mathematical relationships. Ripped out the ML, coded up the operators versions with some slightly tuned up statistics and got dramatically better results.

tamagotchiguy 11mo ago

On a sidenote, love the look and feel of your page!!

kqr 11mo ago

For beginners to SPC, I wrote a practitioner's guide a couple of years ago. Might provide meaningful context: https://entropicthoughts.com/statistical-process-control-a-practitioners-guide https://entropicthoughts.com/statistical-process-control-a-p...

kasperset 11mo ago

Classical stats is still bread and butter for lot of smallish dataset in clinical datasets. It is hard to do machine learning or even regression on some very preliminary data. Metadata is tough to collect and harmonize so it becomes hard to integrate specially with human studies with rare diseases.

world2vec 11mo ago

I'm old enough that I got my Lean Six Sigma Green Belt certification and used Minitab (!!!) to do a statistical process control project on some investment banking back office process that was being automated. Does anyone here even remember Minitab? You kids and your newfangled Python!

srean 11mo ago
My uni had its manual in its library is all I can say :)
8x 11mo ago
I got a six sigma green belt using minitab in ~2018. It's still in use and getting updates, for example they now provide an interface to python: https://pypi.org/project/mtbpy/ https://pypi.org/project/mtbpy/
bmez 11mo ago
Minitab had a good SPC toolbox back in the day. For the longest time, open source solutions were incomplete in the sense that all of them did x-bar/S/R and then usually never got to the more esoteric but handy stuff. Multivariate, even less support.
nxobject 11mo ago
I had the privilege of working at a JMP shop! It still gives me the warm and fuzzies.

hbarka 11mo ago

In the real world, data is never this clean. Majority of the time is data quality work because you will see outliers that might be due to measurement error, calibration, process changes. It requires familiarity with the process and having an understanding and intuition for why the shape of the data distribution in a process is the way it is. Because ad-hoc data visualization and exploration is critical here, enterprise requirements need mature tools that can be used quickly. BYOT and DIY code for an SPC Cpk chart is not what you want to be doing.

runawayprocess 11mo ago

SPC is so great! Far simpler than many other toolsets that don’t work well.

drumnerd 11mo ago

Many type errors