7 ms·

This will make a bunch of startup's life really hard. I think it makes it harder to justify investing in your own ML pipeline or even building your own models f

by petard 7y ago

This will make a bunch of startup's life really hard. I think it makes it harder to justify investing in your own ML pipeline or even building your own models for many use cases.

eyeball 7y ago
Seems like trouble for the likes of H2o (Driverless AI) and DataRobot.
pplonski86 7y ago
I'm running a startup which offers the same solution as Google AuotML Tables. Recently I decided to go open source. I will need to compare my solution with Google AutoML Tables (compare in terms of final model accuracy). But anyway I think many times the best model accuracy is not the most important in ML solutions. Any ideas what can I do with such a situation with my solution? Can I compete with Google?
- deleted 7y ago
  [deleted]
- mgreg 7y ago
  Like anything you will need to focus on your differentiation and doing what others cannot or will not do. For instance Google requires you to bring your data to them (GCP). In many cases it may be better/easier to move the compute rather than the data. Perhaps you can bring your system to a customer's data more easily?
- streetcat1 7y ago
  I am too building a new automl platform on top of kubernetes, I do not think that you can compete with google on alg accuracy, since most of the underlying ML alg are open source (scikit learn or tensor flow). This is not a secret sauce. That said, to get better models, you will probably need to find better hyper parameters tuning method, which depends on the number of models that you are willing to run per model tuning session. So if you have a unique model search method, you can save 10X-100X search time, which translate to real saving. In addition, the solution lacks in model management. In general, most business people would like to understand why a specific model make a specific prediction. Most ops people want to track the training data version, model version, alg version etc. Moreover, The product itself has "best practice" page : https://cloud.google.com/automl-tables/docs/data-best-practices https://cloud.google.com/automl-tables/docs/data-best-practi... which include a list of features that are not in the product. And one of the biggest differentiation should be on-prem vs cloud. Are customers willing to put their data in google (or any other cloud, for that matter) ? Can they legally do that? I think that this product actually benefit the ecosystem since it helps to create a category of auto ml for tabular data, backed by google marketing budget.
  - RosanaAnaDana 7y ago
    Yeah I don't think I agree with the assessment that better models are a function of finding better hyper parameters (entirely). Like, in my limited experienced,stacking and packing models (the 'lift' required by individual models), adjusting ones thinking regarding 'how' they go from data-in >> product out and 'what' it is were trying to predict, pipe-lining annotation and scaling its distribution, are far more effective at generating better ML models than tuning hyper parameters. Generally, if a model is not performing or generating unexpected results, its almost always the data or how the question is being structured.
- holoduke 7y ago
  Usually google is good in the initial release of a product, but they lack good customer care and support. They have non transparant pricings and they disrespect privacy. Those are something's you can differentiate on
  - pplonski86 7y ago
    Thank you! I've just played with AutoML Tables and I got feeling that my service is way too much better than google beta in case of the UI. I'm waiting right now for their model performance score. But I got feeling that they are only tuning Neural Networks. (however I cant find info about algorithms they are using). Maybe this is crazy, but I feel that I can compete with them on model accuracy and UI. For sure, I cant compete with them on marketing.
  - sodosopa 7y ago
    > lack good customer care and support. Depends on how much you're paying them and the SOW you've signed. > non transparant pricings and they disrespect privacy. For pricing, talk with them. Or use a cloud broker.
    - tatersolid 7y ago
      > For pricing, talk with them. You’re joking, right? Every other day there’s a top 10 article on HN about Google locking out a whole business customer with no humans to speak with.
      - sodosopa 7y ago
        Far from it. If you have an actual sow and contract with them, you can define your support terms and get better support than if you just sign up with a business account and avoid talking with salespeople.
- filoleg 7y ago
  Check this comment in the thread https://news.ycombinator.com/item?id=19627515 https://news.ycombinator.com/item?id=19627515 . If your solution allows for embedded devices and other use cases that the solution from Google prohibits (which is a lot of scenarios, actually), then your offering has a chance. Considering that it isn't a matter of choice at this point, with many companies legally not being able to use their offering, you will be doing quite fine. Add on top of that better customer support, like other replies suggested, and your product won't be dying due to competition from Google any time soon.
- sly010 7y ago
  You might consider a strategy where you offer a drop-in compatible service for people not reading Google's terms and not understanding the limitations. Let Google do the marketing for you, people will come to you, when Google kicks them out of their smart TV
- sinatra 7y ago
  Google educates the market for a solution like theirs (or yours). Then, for everyone who thinks they don't wanna rely on Google due to their history of product abandonment or can't send their data to Google, they look for open source solutions that they can self-host. You want to be there for those people as the best open source solution. This may not be a death-knell. This may be your biggest opportunity to get traction in this space!
- blihp 7y ago
  Go vertical rather than horizontal with your solution. Google, and the other large players, need broad swaths of customers/users to get to a viable total addressable market at their scale. That is your advantage over them: you can survive and thrive in much smaller markets. Also, see ricklamers take in this thread (https://news.ycombinator.com/user?id=ricklamers https://news.ycombinator.com/user?id=ricklamers)... it's not the be-all, end-all and never will be. If you've already decided to open source your tools, then is that really even your primary value add[1] or is it some expertise/service on top of your tools? Figure out what that is, be a cockroach and find a market that's too small for them to be bothered with and then super-serve that market. [1] If the tool is your primary value add, I think you're making a mistake in open sourcing it.
  - pplonski86 7y ago
    The tool is primary value added but I would like to have wide adoption. It's my goal to see people using it. I hope I will make money on companies that would use the tool (will make money on additional features and support).
    - blihp 7y ago
      That's obviously your choice. Just be aware you're prioritizing (hopefully) getting more widespread adoption most likely at the cost of the business. The people likely to make money on it will be someone other than you. That's just the harsh reality of open source.
- smadurange 7y ago
  Also, ride on Google's marketing campaign. When Google does this, a lot of uninitiated clients want to get on the bandwagon and use something similar. Before Google, they might not even understand what your platform really offers. I saw the same thing happen when they advertised Stadia. Below is an excerpt their T&C, use those to your advantage. I can't imagine any serious enterprise customer wanting to tie them down to Google. Just tell the market that you do exactly the same as this platform without tieing them down, without privacy violation and without letting them down by abandoning the product (yours is open source) God speed! 12.1 The following terms apply only to current and future Google Cloud Platform Machine Learning Services specifically listed in the "Google Cloud Platform Machine Learning Services Group" category on the Google Cloud Platform Services Summary page: Customer will not, and will not allow third parties to: (i) use these Services to create, train, or improve (directly or indirectly) a similar or competing product or service or (ii) integrate these Services with any applications for any embedded devices such as cars, TVs, appliances, or speakers without Google's prior written permission. These Services can only be integrated with applications for the following personal computing devices: smartphones, tablets, laptops, and desktops
- tixocloud 7y ago
  There’s plenty of room to compete with Google and it makes things easier when it comes to market education. We’re also a startup in the ML space but solely focused on production deployment. You’re right in that model accuracy is not the most important thing. There are many other considerations as to why a model should be used including training time, processing costs, value. Furthermore, as someone mentioned earlier, Google’s service is horrendous and provides another angle to address. Their reputation as a company to be trusted with data is also somewhat shaky given all the privacy concerns.
CamperBob2 7y ago
This will make a bunch of startups' lives really hard... when Google gets bored with it and kills it just when a lot of startups have come to depend on it.
- sodosopa 7y ago
  This makes Google money, Google Reader did not. That's why they will not be bored with AI for a decade or so.
  - wolco 7y ago
    It depends on how the market responds. While AI will be here in 10 years this product will be outdated long before that point looking ahead it almost begs to be shutdown and replaced with a newer version. If you build your business on this platform your business must be willing to evolve with it.
eanzenberg 7y ago
Most people including ones here don't understand the value prop. In ML, the model (and modeling) is an afterthought that takes 3 lines of code. The value-add isn't the model or modeling, it's the feature generation and methodology. When (and if) this gets automated then yes, it will make life hard on many data scientists. But I don't see it happening within 20 years and most likely not within my lifetime.
- streetcat1 7y ago
  I am not sure why you do not see that happening. Driverless AI from h2o does a full feature engineering search. Also, I assume that you want to automate the features search in order to prune the non signal. I am not sure what you mean by methodology? The one thing that I, as a tool developer, do not understand is how humans would be able to handle hundreds of models, including retraining/monitoring and deployment without automl.
  - eanzenberg 7y ago
    Not every AI problem is computer vision or NLP.
- RandyRanderson 7y ago
  Completely agree. This needs to be the top comment.

by petard 7y ago

This will make a bunch of startup's life really hard. I think it makes it harder to justify investing in your own ML pipeline or even building your own models for many use cases.

eyeball 7y ago

Seems like trouble for the likes of H2o (Driverless AI) and DataRobot.

pplonski86 7y ago

I'm running a startup which offers the same solution as Google AuotML Tables. Recently I decided to go open source. I will need to compare my solution with Google AutoML Tables (compare in terms of final model accuracy). But anyway I think many times the best model accuracy is not the most important in ML solutions. Any ideas what can I do with such a situation with my solution? Can I compete with Google?

deleted 7y ago
[deleted]
mgreg 7y ago
Like anything you will need to focus on your differentiation and doing what others cannot or will not do. For instance Google requires you to bring your data to them (GCP). In many cases it may be better/easier to move the compute rather than the data. Perhaps you can bring your system to a customer's data more easily?
streetcat1 7y ago
I am too building a new automl platform on top of kubernetes, I do not think that you can compete with google on alg accuracy, since most of the underlying ML alg are open source (scikit learn or tensor flow). This is not a secret sauce. That said, to get better models, you will probably need to find better hyper parameters tuning method, which depends on the number of models that you are willing to run per model tuning session. So if you have a unique model search method, you can save 10X-100X search time, which translate to real saving. In addition, the solution lacks in model management. In general, most business people would like to understand why a specific model make a specific prediction. Most ops people want to track the training data version, model version, alg version etc. Moreover, The product itself has "best practice" page : https://cloud.google.com/automl-tables/docs/data-best-practices https://cloud.google.com/automl-tables/docs/data-best-practi... which include a list of features that are not in the product. And one of the biggest differentiation should be on-prem vs cloud. Are customers willing to put their data in google (or any other cloud, for that matter) ? Can they legally do that? I think that this product actually benefit the ecosystem since it helps to create a category of auto ml for tabular data, backed by google marketing budget.
- RosanaAnaDana 7y ago
  Yeah I don't think I agree with the assessment that better models are a function of finding better hyper parameters (entirely). Like, in my limited experienced,stacking and packing models (the 'lift' required by individual models), adjusting ones thinking regarding 'how' they go from data-in >> product out and 'what' it is were trying to predict, pipe-lining annotation and scaling its distribution, are far more effective at generating better ML models than tuning hyper parameters. Generally, if a model is not performing or generating unexpected results, its almost always the data or how the question is being structured.
holoduke 7y ago
Usually google is good in the initial release of a product, but they lack good customer care and support. They have non transparant pricings and they disrespect privacy. Those are something's you can differentiate on
- pplonski86 7y ago
  Thank you! I've just played with AutoML Tables and I got feeling that my service is way too much better than google beta in case of the UI. I'm waiting right now for their model performance score. But I got feeling that they are only tuning Neural Networks. (however I cant find info about algorithms they are using). Maybe this is crazy, but I feel that I can compete with them on model accuracy and UI. For sure, I cant compete with them on marketing.
- sodosopa 7y ago
  > lack good customer care and support. Depends on how much you're paying them and the SOW you've signed. > non transparant pricings and they disrespect privacy. For pricing, talk with them. Or use a cloud broker.
  - tatersolid 7y ago
    > For pricing, talk with them. You’re joking, right? Every other day there’s a top 10 article on HN about Google locking out a whole business customer with no humans to speak with.
    - sodosopa 7y ago
      Far from it. If you have an actual sow and contract with them, you can define your support terms and get better support than if you just sign up with a business account and avoid talking with salespeople.
filoleg 7y ago
Check this comment in the thread https://news.ycombinator.com/item?id=19627515 https://news.ycombinator.com/item?id=19627515 . If your solution allows for embedded devices and other use cases that the solution from Google prohibits (which is a lot of scenarios, actually), then your offering has a chance. Considering that it isn't a matter of choice at this point, with many companies legally not being able to use their offering, you will be doing quite fine. Add on top of that better customer support, like other replies suggested, and your product won't be dying due to competition from Google any time soon.
sly010 7y ago
You might consider a strategy where you offer a drop-in compatible service for people not reading Google's terms and not understanding the limitations. Let Google do the marketing for you, people will come to you, when Google kicks them out of their smart TV
sinatra 7y ago
Google educates the market for a solution like theirs (or yours). Then, for everyone who thinks they don't wanna rely on Google due to their history of product abandonment or can't send their data to Google, they look for open source solutions that they can self-host. You want to be there for those people as the best open source solution. This may not be a death-knell. This may be your biggest opportunity to get traction in this space!
blihp 7y ago
Go vertical rather than horizontal with your solution. Google, and the other large players, need broad swaths of customers/users to get to a viable total addressable market at their scale. That is your advantage over them: you can survive and thrive in much smaller markets. Also, see ricklamers take in this thread (https://news.ycombinator.com/user?id=ricklamers https://news.ycombinator.com/user?id=ricklamers)... it's not the be-all, end-all and never will be. If you've already decided to open source your tools, then is that really even your primary value add[1] or is it some expertise/service on top of your tools? Figure out what that is, be a cockroach and find a market that's too small for them to be bothered with and then super-serve that market. [1] If the tool is your primary value add, I think you're making a mistake in open sourcing it.
- pplonski86 7y ago
  The tool is primary value added but I would like to have wide adoption. It's my goal to see people using it. I hope I will make money on companies that would use the tool (will make money on additional features and support).
  - blihp 7y ago
    That's obviously your choice. Just be aware you're prioritizing (hopefully) getting more widespread adoption most likely at the cost of the business. The people likely to make money on it will be someone other than you. That's just the harsh reality of open source.
smadurange 7y ago
Also, ride on Google's marketing campaign. When Google does this, a lot of uninitiated clients want to get on the bandwagon and use something similar. Before Google, they might not even understand what your platform really offers. I saw the same thing happen when they advertised Stadia. Below is an excerpt their T&C, use those to your advantage. I can't imagine any serious enterprise customer wanting to tie them down to Google. Just tell the market that you do exactly the same as this platform without tieing them down, without privacy violation and without letting them down by abandoning the product (yours is open source) God speed! 12.1 The following terms apply only to current and future Google Cloud Platform Machine Learning Services specifically listed in the "Google Cloud Platform Machine Learning Services Group" category on the Google Cloud Platform Services Summary page: Customer will not, and will not allow third parties to: (i) use these Services to create, train, or improve (directly or indirectly) a similar or competing product or service or (ii) integrate these Services with any applications for any embedded devices such as cars, TVs, appliances, or speakers without Google's prior written permission. These Services can only be integrated with applications for the following personal computing devices: smartphones, tablets, laptops, and desktops
tixocloud 7y ago
There’s plenty of room to compete with Google and it makes things easier when it comes to market education. We’re also a startup in the ML space but solely focused on production deployment. You’re right in that model accuracy is not the most important thing. There are many other considerations as to why a model should be used including training time, processing costs, value. Furthermore, as someone mentioned earlier, Google’s service is horrendous and provides another angle to address. Their reputation as a company to be trusted with data is also somewhat shaky given all the privacy concerns.

CamperBob2 7y ago

This will make a bunch of startups' lives really hard... when Google gets bored with it and kills it just when a lot of startups have come to depend on it.

sodosopa 7y ago
This makes Google money, Google Reader did not. That's why they will not be bored with AI for a decade or so.
- wolco 7y ago
  It depends on how the market responds. While AI will be here in 10 years this product will be outdated long before that point looking ahead it almost begs to be shutdown and replaced with a newer version. If you build your business on this platform your business must be willing to evolve with it.

eanzenberg 7y ago

Most people including ones here don't understand the value prop. In ML, the model (and modeling) is an afterthought that takes 3 lines of code. The value-add isn't the model or modeling, it's the feature generation and methodology. When (and if) this gets automated then yes, it will make life hard on many data scientists. But I don't see it happening within 20 years and most likely not within my lifetime.

streetcat1 7y ago
I am not sure why you do not see that happening. Driverless AI from h2o does a full feature engineering search. Also, I assume that you want to automate the features search in order to prune the non signal. I am not sure what you mean by methodology? The one thing that I, as a tool developer, do not understand is how humans would be able to handle hundreds of models, including retraining/monitoring and deployment without automl.
- eanzenberg 7y ago
  Not every AI problem is computer vision or NLP.
RandyRanderson 7y ago
Completely agree. This needs to be the top comment.