7 ms·

I work for a startup[0] that does quality control through machine learning for the beverage industry. We're focused most on beer, followed by coffee and spirits

by evandev 11y ago

I work for a startup[0] that does quality control through machine learning for the beverage industry. We're focused most on beer, followed by coffee and spirits. [0]: https://gastrograph.com https://gastrograph.com

andr3w321 11y ago
Where do you find these ML wizard conusltants? I've been looking to hire someone part time for a project to help build a model but most good people have no time and I don't trust that anyone on elance or similar is any good.
- nalourie 11y ago
  Depending on the project / time line I'd be interested to help. Is there a way to get in touch?
- andr3w321 11y ago
  Whoops on phone and replied to wrong comment. If anyone is interested contact me andr3w321@gmail.com
- davmre 11y ago
  Email older PhD students at your local university, assuming you have a strong local university. Describe your problem in a few sentences to get them interested, and offer competitive compensation ($100-$200/hr depending on location) in exchange for a few hours per week of their time.
- JasonCEC 11y ago
  The short answer is: _you don't_. That's not how machine learning works at scale. To quote from a great recent article: (I can't find the link, sorry) It's one thing to create an excellent fraud detection model in R, and quite another to build: - Fault-tolerant ingest of live data at scale that could represent fraudulent actions - Real-time computation of features based on the data stream - Serialization, versioning and management of a fraud detection model - Real-time prediction of fraud based on computed features at scale - Learning over all historical data - Incremental update of the production model in near-real-time - Monitoring, testing, productionization of all of the above You don't build a data team out of a single person and tack on an easy model to build a company - it takes a team to build a real data project, and those teams are hard to find, hard to recruit, and hard to make successful.
- evandev 11y ago
  We're based near a large University and hire Masters/PhD students about to graduate first as a part time/intern then full-time. It has worked out pretty well for us.
JasonCEC 11y ago
Evan beat me to it :) Evan is our Lead Engineer, and I'm the CEO. We believe vertical integrated SaaS focused on prediction will reshape a lot of industries.
- exelius 11y ago
  I believe that too. But I also believe that large ERP vendors will be the ones delivering this value, because integrating predictive modeling into a company's operating model is the hard part. They're also a lot more vertically integrated, plus their products work with your existing ERP / forecasting system. Though it does seem like you guys have found a niche - quality automation in general is booming right now, and ML predictive models can be very helpful in certain industries like food and beverage. I'm guessing you guys probably work with smaller/younger companies that don't have the kinds of problems your Kraft/P&G/J&J do - there's certainly plenty of opportunity there. "Prediction" in general can be hard - the biggest problem can be getting good data in the first place, and fixing a company's data gathering problems is out of scope of any sane software shop. But it's right in the wheelhouse of an IT consulting shop, which is where I'm seeing a lot of ML guys end up.
  - JasonCEC 11y ago
    Thanks for the reply! We work across the industry in alcohol production, so small craft to giant global sized companies. A lot of what we do is implement better data acquisition from sensory, production, distribution, and demographics during our implementation phase, before our prediction or optimization models have a good enough baseline to run - we are definitely not a sane software shop! ERPs are a threat but not direct competition to us - we're hardcore "best of breed" software, and it will be interesting to see how those large companies respond to us in the future...
    - exelius 11y ago
      Ah; so you guys found a consulting model for it :) Consulting models aren't bad (indeed - they can be very profitable); but they don't hockey-stick like people think software does. This makes them unattractive to VCs, but they're a good space for bootstrapped startups (aka "small businesses").

by evandev 11y ago

andr3w321 11y ago

Where do you find these ML wizard conusltants? I've been looking to hire someone part time for a project to help build a model but most good people have no time and I don't trust that anyone on elance or similar is any good.

nalourie 11y ago
Depending on the project / time line I'd be interested to help. Is there a way to get in touch?
andr3w321 11y ago
Whoops on phone and replied to wrong comment. If anyone is interested contact me andr3w321@gmail.com
davmre 11y ago
Email older PhD students at your local university, assuming you have a strong local university. Describe your problem in a few sentences to get them interested, and offer competitive compensation ($100-$200/hr depending on location) in exchange for a few hours per week of their time.
JasonCEC 11y ago
The short answer is: _you don't_. That's not how machine learning works at scale. To quote from a great recent article: (I can't find the link, sorry) It's one thing to create an excellent fraud detection model in R, and quite another to build: - Fault-tolerant ingest of live data at scale that could represent fraudulent actions - Real-time computation of features based on the data stream - Serialization, versioning and management of a fraud detection model - Real-time prediction of fraud based on computed features at scale - Learning over all historical data - Incremental update of the production model in near-real-time - Monitoring, testing, productionization of all of the above You don't build a data team out of a single person and tack on an easy model to build a company - it takes a team to build a real data project, and those teams are hard to find, hard to recruit, and hard to make successful.
evandev 11y ago
We're based near a large University and hire Masters/PhD students about to graduate first as a part time/intern then full-time. It has worked out pretty well for us.

JasonCEC 11y ago

Evan beat me to it :) Evan is our Lead Engineer, and I'm the CEO. We believe vertical integrated SaaS focused on prediction will reshape a lot of industries.

exelius 11y ago
I believe that too. But I also believe that large ERP vendors will be the ones delivering this value, because integrating predictive modeling into a company's operating model is the hard part. They're also a lot more vertically integrated, plus their products work with your existing ERP / forecasting system. Though it does seem like you guys have found a niche - quality automation in general is booming right now, and ML predictive models can be very helpful in certain industries like food and beverage. I'm guessing you guys probably work with smaller/younger companies that don't have the kinds of problems your Kraft/P&G/J&J do - there's certainly plenty of opportunity there. "Prediction" in general can be hard - the biggest problem can be getting good data in the first place, and fixing a company's data gathering problems is out of scope of any sane software shop. But it's right in the wheelhouse of an IT consulting shop, which is where I'm seeing a lot of ML guys end up.
- JasonCEC 11y ago
  Thanks for the reply! We work across the industry in alcohol production, so small craft to giant global sized companies. A lot of what we do is implement better data acquisition from sensory, production, distribution, and demographics during our implementation phase, before our prediction or optimization models have a good enough baseline to run - we are definitely not a sane software shop! ERPs are a threat but not direct competition to us - we're hardcore "best of breed" software, and it will be interesting to see how those large companies respond to us in the future...
  - exelius 11y ago
    Ah; so you guys found a consulting model for it :) Consulting models aren't bad (indeed - they can be very profitable); but they don't hockey-stick like people think software does. This makes them unattractive to VCs, but they're a good space for bootstrapped startups (aka "small businesses").