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Google launches an end-to-end AI platform
- fxfan 8y agoBets on: 1) When it will be shutdown? 2) When it will get ads? EDIT: Does google have a paid shill force on hacker news? They are copying well from the IBM and Apple playbook
- deleted 8y ago[deleted]
- narrator 8y ago3) How many times a month will the API you're using get deprecated and how much developer time will you have to dedicate to keeping up with Google's API changes?
- trulyrandom 8y agoWhy do you expect it to get ads? The vast majority of Google products are ad-free, apart from Search and YouTube.
- LeonM 8y agoAnd Gmail
- satokema_work 8y agothe ads aren't directly in the product, but that data ends up trying to provide you with "relevant" ads, just like the Android phone itself. Are people honestly not creeped out when you get ads from other people's phones?
- minimaxir 8y agoIt's worth noting that earlier in the Google Next keynote, they had a slide on Privacy and that they will not access your data in GCP unless you request that they do so.
- ijpoijpoihpiuoh 8y agoI wish that people would stop with this unproductive line of commentary. Google shuts down consumer products sometimes. Pruning back the set of unprofitable things you're working on is good business. Expect to see Google and literally every other successful company continue to take this approach to unprofitable lines of business. Also, how would putting ads on a developer platform be profitable? It's far more likely that they would charge money for it.
- malux85 8y agoGoogle is distinct in their bad attitude though. What it feels like is: An engineer at google builds something cool. Management decide it could be a product, and a bit of tooling here and there and BAM it’s released. It goes well, then it stays. It goes poorly or even just average, then it gets thrown out. And what of the users who put faith in the new product? Screw them it’s not profitable enough. Compare to AWS, an engineer writes something cool, it gets evaluated by a project team, the business benefit and support infra is put in place, its trialed with a few users, its honed, then if it gets good KPIs then it’s generalised and released as a product. They subset very slowly and always make sure there’s a clear migration path, with humans in support to help the developers if need be. Saying “Google and literally every other successful company continue to take this approach to unprofitable lines of business” is too reductive : nobody is contesting that a business shouldn’t cut out unprofitable actions, but the repeated, drastic culling and the “f-you we know best” attitude they take makes developers distrust google. Google is too whimisical with both releasing and retiring products.
- ijpoijpoihpiuoh 8y ago> Compare to AWS AWS is not a consumer-facing organization. The standards there are entirely different. Google Cloud has a much more stable offering than Google writ large. Very few of the products on gcemetery.co are cloud-related.
- stingraycharles 8y agoHow is AWS not being consumer facing relevant? Both Google and Amazon started out being consumer facing and now have a cloud division. Their cloud divisions not being consumer facing is irrelevant.
- sctb 8y ago> Please don't impute astroturfing or shillage. That degrades discussion and is usually mistaken. If you're worried about it, email us and we'll look at the data. > Don't introduce flamewar topics unless you have something genuinely new to say. Avoid unrelated controversies and generic tangents. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- fxfan 8y agoSorry!
- minimaxir 8y agoThis is being announced now in the Google Next keynote. This platform focuses not on the this-AI-is-magic-and-can-solve-everything like many AI SaaS startups announced on Hacker News, but focuses on how to actually integrate this AI into production workflows, which is something I wish was discussed more often in AI. The announcements here, including AutoML Tables (which is coincidentally similar to my own Python package: https://news.ycombinator.com/item?id=19492406 https://news.ycombinator.com/item?id=19492406), the new BigQuery BI tools, and the new Google Sheets integrations, make me a very happy data scientist. I'm taking the rest of the week to figure out how to integrate everything announced into my team.
- deleted 8y ago[deleted]
- mmq 8y agoHi @minimaxir, I just checked the readme of your project. It looks great, there's a mention to integrate Polyaxon[0] for running jobs in distributed way on Kubernetes. Let me know if you need any help with that. [1]: https://github.com/polyaxon/polyaxon/ https://github.com/polyaxon/polyaxon/
- minimaxir 8y agoSure! :)
- amrrs 8y agoLooks like Google is taking over Cloud (from AWS) for AI by building an ecosystem and building tools for non Data scientists - consumer level product. Surely IBM can do similar thing with their recent Redhat acquisition, but will they ?
- streetcat1 8y agoI am not sure that there is a connection between redhat and AI?
- cloakandswagger 8y ago
- silasdavis 8y agoParaphrased from the article: AutoML tables takes a generic table and predicts can predict a columns value (for an unseen partial row, I assume). Their product page emphasises how easy it is: for developers with limited machine learning experience. What are the consequences of using such structured data predictions devoid of interpretation or quantified uncertainty? Presumably such predictions could be from a set of discrete values, how is that smoothed? What is the result of providing definite results in the absence of intention?
- perturbation 8y agoAutoML is essentially training a ML model using some heuristics or optimization algorithm to select model architecture and train a model. Feature engineering / feature synthesis as well as interpretability remain open challenges. If I'm understanding your questions correctly, the main problems I see with this are: - Using raw data instead of feature engineering (less of a problem given feature synthesis libraries like https://www.featuretools.com/ https://www.featuretools.com/ and other heuristic methods). I'd expect Google to do a good job of basic things like normalization of raw input features before training. - Using features that it really shouldn't (if you just throw ML at your database for say, loan applications, then sensitive / personally identifying information can/will be used as features) - Lack of insight / understanding as to what is driving the model. This can be partially overcome with post-training methods like LIME, Shapley values, etc. I wouldn't expect predictions to be from a set of discrete values - if (say) predicting housing values and training a NN, the output should be continuous and based on the input features.
- mritchie712 8y agoAnother common error I see is timing (e.g. using data from the "future" to predict an event). To build on your loan example, if you inadvertently included the current FICO score of an applicant that applied 12 months ago, it will be unfairly correlated with the loans current performance.
- kmax12 8y ago
- petard 8y agoThis 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 8y agoSeems like trouble for the likes of H2o (Driverless AI) and DataRobot.
- pplonski86 8y agoI'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 8y ago[deleted]
- mgreg 8y agoLike 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 8y agoI 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.
- sgt101 8y agoTMForum are developing an end to end standard for model dev and management, unfortunately it's closed but I really think that standardised components (feature store, model store, model registry) and model non functionals (identity, use history, status) with standardised API's are pretty necessary. BTW : deployed to prod is only 50% of the story; someone poor fool has to manage (and account for) these things in life!
- mlindner 8y agoI'm really not happy to see this. This further entrenches people into Google's AI platform where all the input data will be harvested for ad revenue and even further profiling of human data. Laws enshrining natural rights of privacy of personal data can't come soon enough.
- nealmueller 8y agoGoogle Cloud does not use customer data for Google's search graph. No cloud does this. From Google: "At Google Cloud, we do not access customer data for any reason other than those necessary to fulfill our contractual obligations to you. Technical controls require valid business justifications for any access by support or engineering personnel to your content. Google also performs regular audits of accesses by administrators as a check on the effectiveness of our controls." Justification reason codes for data access https://cloud.google.com/logging/docs/audit/reading-access-transparency-logs#justification_reason_codes https://cloud.google.com/logging/docs/audit/reading-access-t... That said, I agree with what you've said here: "Laws enshrining natural rights of privacy of personal data can't come soon enough." Nicely put.
- burtonator 8y agoI'm thinking of integrating this into Polar for people that use our cloud sync support. I should be able to use the documents and their tags to build more derivative tag data so that when I'm given a new document I can compute a set of automated tags for the user. This way when you save a document to your repository it's given some suggested tags.
- crappy-doo 8y agoand literally nobody cared...
- deleted 8y ago[deleted]
- andrewtbham 8y agoI have tried Google AutoML, Microsoft cognitive services and looked at Clarifai for image labeling and classification. I ended up writing my own system. Maybe this new Google product will be better... definitely room for improvement in this space.
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- crooked-v 8y agoNeat. How long until they shut it down?
- andybak 8y agoPlease don't post glib cliched comments. There's a valid point behind this but the same one-liner appearing several times on every Google product announcement is just tiresome.
- crooked-v 8y agoYou say 'glib', but I actually want the answer to the question, so I know whether to bother investing any time learning about this or not.
- FridgeSeal 8y agoGiven that it essentially takes ML product/tool and removes literally all agency and control or tuning you have over it and puts it entirely in Google’s hands, I’d avoid it anyway.
- Gigablah 8y ago"but I actually want the answer to the question" No, you don't. Because any rational person would know that nobody is going to have or tell you the answer. So, still glib and disingenuous to boot.
- FridgeSeal 8y agoI mean, it’s a fairly valid point. Google has a reputation for doing this... Google is the one to blame for this being tiresome.
- kerng 8y agoGoogle states it's a beta tool, that should set expectations right. Its worthwhile reminding consumers to not jump on the next Google bandwagon blindly. Its Google's job to convince us wrong, looking at this should help get perspective: https://killedbygoogle.com/ https://killedbygoogle.com/
- siavash 8y agoMake sure you've read the service terms[1] if you plan on building apps for speakers, cars, TVs or smart watches... 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 [1] https://cloud.google.com/terms/service-terms#12-google-cloud-platform-machine-learning-group-and-google-cloud-machine-learning-engine https://cloud.google.com/terms/service-terms#12-google-cloud...
- thinkingkong 8y agoThat's fascinating. When you have that many services, platforms, company divisions, etc you end up naturally competing with yourself, or enabling your competition.
- jimrandomh 8y agoI have a hard time imagining who this wouldn't be a dealbreaker for. These terms also mean you can't use it for open source, and you can't use it if you don't know what the ultimate application is going to be. And you probably can't resell technology you create, because no one who buys it is going to want that restriction, either.
- zeroxfe 8y agoIt's not a dealbreaker for in-house ML applications particularly in the enterprise (banks, telcos, etc.), which is a huge market for cloud providers.
- sodosopa 8y agoThat's how my company would apply the platform to our work. My boss is there now, will be interesting to see what he find out vs what we're doing with our current IBM platform.
- meeks 8y agoDoes any of this announcement affect Google Datalab? It feels like they keep Datalab as some second class citizen, that doesn't even get a spot in the menu bar in the cloud console and doesn't get any love when all of these let-us-build-the-model-for-you products get all of these announcements and upgrades.
- chibg10 8y agoI work in building and deploying production ML/AI models but I'm having a lot of trouble cutting through the marketing jargon in this article and on Google's website as well. Can someone explain what this does in engineering terms? How does this differ from something like AWS Sagemaker?
- streetcat1 8y agoSo for me, automl means machine learning without writing a single line of python code or any code. I.e. data and schema IN, deployed models OUT. With sagemaker you still need to provide the python code for training.
- cle 8y agoSageMaker has a wide array of built-in training algorithms: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
- jedberg 8y agoThis is where Google really has a big lead on AWS -- the AI space. AWS has AI tools, but Google's are better and easier to use. The big question is: If all your data sits in AWS, because your app that generates the data is there, do you reach across and try to use the Google AI tools, or are their tools compelling enough to get you to move your app and all your data to GCS?
- agentofoblivion 8y agoWhat AI tools? SageMaker is awesome. What can Google do for me? All I've seen from them is marketing hype. I also haven't looked very hard.
- agentofoblivion 8y agoNote: this is a real question with real curiosity. What are googles best AI tools?
- solidasparagus 8y agoIn what way are Google's better and easier to use? Which tools? I haven't fully explored both, but they look very similar in terms of features to me.
- mlthoughts2018 8y agoIt’s so disingenuous for Google to brand these efforts as “democratizing AI.” It is precisely the opposite. This is classic commoditization of your complement. On one hand, Google is pushing to centralize the integration, data management and computing platforms for machine learning, so that these things become as much of a commodity as possible. On the other side, they are offering massive compensation packages or acqui-hiring as much AI talent as they can, not really because they have useful work for these folks to do, but to artificially reduce the supply of statistical algorithm talent, making their consulting and pre-packaged AI solutions go up in value in a manner that is pretty much the same as De Beers pushing silos full of diamonds to keep diamond prices artificially high. This is very much the opposite of democratization, and my advice to anyone considering services like this or like Amazon’s out of the box models, don’t do it! If you think it’s going to save you money over paying competitively to get your own in-house machine learning staff, you’re wrong, and you’re going to waste probably hundreds of thousands of dollars before you learn you’re wrong.
- m0zg 8y ago>> It’s so disingenuous for Google to brand >> these efforts as “democratizing AI.” Exactly. NVIDIA building fast consumer GPUs and CUDA/cuDNN is "democratizing AI". FB (and Google) releasing open source deep learning toolkits is "democratizing AI". People releasing reproducible research code and datasets are "democratizing AI". Cloud vendor lock-in and proprietary hardware, software, _and_ datasets is not in any way "democratizing" anything. >> not really because they have useful work for these folks to do This, however, is where your argument flies off the rails, IMO. They offer this much because there's very limited supply of people who can do both research and development at the same time. Meaning, they don't just write papers, but also can code pretty well. It's usually one or the other, and hardly ever both. And the total in this case is much greater than the sum of its parts.
- mlthoughts2018 8y agoI agree with you that the supply of those workers is low (I am one of them! And hiring more is extremely hard!) ... but it doesn’t mean Google / FB / etc have meaningful work for them. A friend of mine was hired as a senior ML person at Facebook and ended up working solely on cartoon avatars and page responsiveness / latency optimization (not using ML). When he raised the issue to managers that he wasn’t working on anything related to his specialization (deep learning for NLP) and this made him unhappy, the response was essentially, “Get in line.” I’ve heard similar stories about Google from a former boss who had been a long time manager in Google. You essentially get paid super well to be put out to pasture so that your skill isn’t being used by other companies (leading to more demand for Google’s managed AI solutions). In order to get career-developing work, you have to play political games or get hired in a non-standard way, like acqui-hire or poached, where you can negotiate your projects as part of your hiring conditions. Eventhen it will probably only be respected for a short time while it’s convenient for Google, and they’ll find a way to manage you out of that situation when they want to. There are some tremendously talented AI engineers in places like Google. Some of them create awesome products and tools. A bunch of others sit around and atrophy working on dumb shit locked in golden handcuffs just to ensure they’re not on the market and able to help a company build things in-house more cheaply / more optimally than if they needed to buy it through some managed services through Google.
- alexnewman 8y agoOnly a fool would use any of these platforms and large companies never will. The ml industry is full of fledgling products that no one should ever use and clearly if you use this you will be locked into google forever
- eyeball 8y agosome large companies here: https://www.datarobot.com/customers/ https://www.datarobot.com/customers/
- ricklamers 8y agoI've tried using Google's AutoML to classify medical images as part of a Kaggle competition just to see what their process is like and how well it performs. This was Q4 2018, so things might have changed slightly. In brief, my experience was quite frustrating. First, getting my dataset to the cloud required quite a bit of manual labor. Uploading my 25GB of images on my 40mbit/s wasn't really ideal so I ended up spawning an virtual machine on GCP and downloading directly from Kaggle. Unzipping the files and writing it to Google Cloud storage with some terminal commands. Furthermore, to get the label data into AutoML I had to write some generation script that generated the exact format CSV that AutoML requires - which was hidden in their documentation somewhere with no mention in the AutoML environment itself. Nothing too cumbersome but generally not a very user friendly experience, or something I wish to repeat many times if I get a new/different dataset. Ultimately, when the data with labels was in the model started training. Then I found, that I didn't really have the tools and information to assess the model performance. They did a decent job of characterizing model performance through precision recall graphs and displaying incorrect predictions but that didn't really satisfy me. I was interested in getting more details about where it was misclassifying images, specifically how classification performance was distributed across the 28 classes the model was predicting (in a multi label context). This is the point where I think the downside of working with a platform such as AutoML starts showing. I tried reaching out to someone about gaining more insight into model performance by opening a ticket, since there was no phone number. After a couple of days I finally received an email from a product representative that told me that for any assistance I should contact one of their local cloud partners. These are third party vendors that typically assist companies in deploying cloud based applications in the GCP. However, after calling two of these companies that were highly recommended by Google's vendor page I was told that they don't have any experience with AutoML and that I was on my own. The other company didn't reply at all. In my view, choosing a product such as AutoML - for a company that is serious about adopting AI to improve their business - is currently not a good path (yet). And I see this space as being wide open for competition with current solutions not cutting it from my point of view.
- pplonski86 8y agoI cant find in the documentation what kind of algorithms they are using? Is it only one algorithm type (neural network)?
- Liveanimalcams 8y agoThe point of autoML from what I've gathered is to make it as easy as can be to get a model working in production. AutoML at least AutoML Vision is using transfer learning to retrain X number of layers from their algorithm (the one google uses is escaping me right now). The number of layers it has to retrain is the value they offer, it tries and tries optimizing for accuracy. I've had good results with it, but you do have to do things their way and its not always well documented. If you want more control you should create your own model and host it on google app engine, otherwise AutoML is what it is, no way to customize or tune it other than changing the training data you give it.
- deleted 8y ago[deleted]
- n4s33r 8y agoIn before Google kills this
- AndrewKemendo 8y agoI'd be curious to know who has used one of these end to end "AI" services in production for a successful, market fitting product. From my experience doing this for a handful of companies, it's almost always better long term to use ML libraries that fit into the organizational architecture that exists, rather than outsource the whole ML pipeline. It's a serious amount of lock in to do that. Maybe it's a an easier sell if your entire pipeline is already built into GCP and you're just tacking this on as a parallel path.
- tixocloud 8y agoAgreed with the long term benefits on fitting into existing architecture. I did speak with someone in financial services who’s been all GCP and they are quite impressed by the way everything is integrated and how they do not have to shift data from storage to train.
- kerng 8y agoHow is this different to Azure ML and Amazon's offerings? I know Azure ML as has been out for 3+ years - so I assume they have many features and enterprise learnings baked in over the years. Does anyone have good comparison?
- Gravityloss 8y agoGoogle launches: 1500 HN entries https://hn.algolia.com/?query=google%20launches&sort=byPopularity&prefix&page=0&dateRange=all&type=story https://hn.algolia.com/?query=google%20launches&sort=byPopul... Google shuts: 700 HN entries: https://hn.algolia.com/?query=google%20shuts&sort=byPopularity&prefix&page=0&dateRange=all&type=story https://hn.algolia.com/?query=google%20shuts&sort=byPopulari... (thought it would be funny to compare, it's not very scientific of course...)
- what_ever 8y agoUsing "" will be more correct? https://hn.algolia.com/?query=%22google%20shuts%22&sort=byPopularity&prefix=false&page=0&dateRange=all&type=story https://hn.algolia.com/?query=%22google%20shuts%22&sort=byPo... gives 71 results and that includes shutting down websites as well. Disc: Googler.