Is there anyway to create your own AWS/Azure/GCP on your own server?
Google has Anthos (kubernetes on prem, with access to GCP marketplace).
When AWS, Azure, or GCP Becomes the Competition
81–90 of 92 posts
Re: When AWS, Azure, or GCP Becomes the Competition
#82If you're doing anything ML related your best bet is to niche down and solve a very narrow problem for a specific industry / market segment. Building generic AI tooling sounds great but rarely solves anyones problems.
As an ML practitioner in a large ecommerce company, I totally agree. When I evaluate third party tools, 99% have no use at all for me. Things like Algolia or Rekognition or Clarifai. Even Sagemaker and Fargate are mostly not useful to me as my company can easily operate its own k8s platform. Open source tools for model building are great and a small team of engineers can write whatever odds & ends that aren’t covered…
Could you care to elaborate further on specialized database engines and rapid data annotation tools? There are plenty of data annotation tools out there that seem to do the job so not sure what rapid would mean in this instance.
Re: When AWS, Azure, or GCP Becomes the Competition
#83Earlier quoted context omitted.
Notebooks are a maintainability maze waiting to happen. They're incredibly non-portable; they don't play nice with version control; I know how they're supposed to be used, but I'm yet to see them actually used in a way that isn't a breeding ground for the worst software engineering I've seen.
Yet the same could be said of Excel, which is running the whole financial industry.
It can be both a bad environment and very successful.
Personally I would be happy with just proper version control support.
Re: When AWS, Azure, or GCP Becomes the Competition
#84Earlier quoted context omitted.
I am not. Microsoft was always a partner driven company. Most of their sales are through their partners, and they have learned to respect that. They have also learned to build platform, unlike MacOS/Android, windows will never pull APIs out form under you.
And windows basically sucks, in no small part due to the historical baggage it must carry. That and it’s not nix
That's a very narrow view.
Supporting historical baggage is why a lot of business people trust Windows.
Eg. You can still run VB 6 applications :p
Re: When AWS, Azure, or GCP Becomes the Competition
#85Earlier quoted context omitted.
As an ML practitioner in a large ecommerce company, I totally agree. When I evaluate third party tools, 99% have no use at all for me. Things like Algolia or Rekognition or Clarifai. Even Sagemaker and Fargate are mostly not useful to me as my company can easily operate its own k8s platform. Open source tools for model building are great and a small team of engineers can write whatever odds & ends that aren’t covered…
Could you care to elaborate further on specialized database engines and rapid data annotation tools? There are plenty of data annotation tools out there that seem to do the job so not sure what rapid would mean in this instance.
I mean things like vertica or kdb+ that have specialized performance properties for some use cases. Also to some minor extent managed cluster pipeline tooling like Spark. I don’t mind paying for managed versions of these (not Databricks though). For annotation tools I mean things like Prodigy.
Re: When AWS, Azure, or GCP Becomes the Competition
#86Earlier quoted context omitted.
As an ML practitioner in a large ecommerce company, I totally agree. When I evaluate third party tools, 99% have no use at all for me. Things like Algolia or Rekognition or Clarifai. Even Sagemaker and Fargate are mostly not useful to me as my company can easily operate its own k8s platform. Open source tools for model building are great and a small team of engineers can write whatever odds & ends that aren’t covered…
How do you train your models? With Databricks (which is just a nice notebook UI for Apache Spark) I don't have to spend time setting up VMs and libraries for the distributed training.
Your first sentence seems unrelated to your second sentence, but it seems like you think they are related?
Any platform that advertises something like you don’t have to spend time defining your training environment, whether it’s Databricks or an out of the box deep learning VM on GCP, is a liability waiting to happen. You always need to define your own training environment, especially because you’ll almost always need specific (likely pinned) versions of all your dependencies, including system dependencies, to manage model training as part of a production life cycle. Very often you also need e.g. custom compiled TensorFlow, custom GPU settings or drivers, etc. It’s very foolish to base that environment on whatever comes out of the box.
Spark also is not universally useful. For training small models many times, like a workload that trains hundreds of small models all day (this was a production use case I had before that my company pilot tested with Databricks), the overhead of Py4J connector is insanely bad. It’s a really terrible paradigm for Python software, meanwhile Scala is a miserable ecosystem for production machine learning models. On top of all this, Spark MLib has huge gaps in functionality and whole classes of problems (e.g. large scale MCMC inference) are not solvable in a way in Spark that is seriously comparable to other tools like STAN & pymc running on not-Spark with simple multiprocessing.
Re: When AWS, Azure, or GCP Becomes the Competition
#87Earlier quoted context omitted.
Could you care to elaborate further on specialized database engines and rapid data annotation tools? There are plenty of data annotation tools out there that seem to do the job so not sure what rapid would mean in this instance.
I mean things like vertica or kdb+ that have specialized performance properties for some use cases. Also to some minor extent managed cluster pipeline tooling like Spark. I don’t mind paying for managed versions of these (not Databricks though). For annotation tools I mean things like Prodigy.
Thanks. Why the interest in managed cluster pipeline but not Databricks? They seem to be the big name in the game for Spark. Would some tooling around snapshotting, sequential AB testing, staged rollouts, ghost models be of use?
Re: When AWS, Azure, or GCP Becomes the Competition
#88Earlier quoted context omitted.
I am not. Microsoft was always a partner driven company. Most of their sales are through their partners, and they have learned to respect that. They have also learned to build platform, unlike MacOS/Android, windows will never pull APIs out form under you.
And windows basically sucks, in no small part due to the historical baggage it must carry. That and it’s not nix
True, but an operating system should be a solid foundation to build on, not quicksand.
We now have a situation where a well designed and complete 5 year old application will arbitrarily stop working because someone made an 'improvement' to the OS.
Re: When AWS, Azure, or GCP Becomes the Competition
#89Earlier quoted context omitted.
I mean things like vertica or kdb+ that have specialized performance properties for some use cases. Also to some minor extent managed cluster pipeline tooling like Spark. I don’t mind paying for managed versions of these (not Databricks though). For annotation tools I mean things like Prodigy.
Thanks. Why the interest in managed cluster pipeline but not Databricks? They seem to be the big name in the game for Spark. Would some tooling around snapshotting, sequential AB testing, staged rollouts, ghost models be of use?
Databricks is a notebook frontend to cluster computing. Cluster computing is useful and I’m willing to pay for it as long as I have total & complete control of the environments and tooling used in the cluster workflow.
A notebook frontend however is less than useless, and is actively harmful by propagating poor notebook environments even further into aspects of computing where they cause harm and hurt reproducability and code factoring.
Given this, even if Databricks offered perfectly complete features for all aspects of cluster computing, it would still be inferior to just my own managed EC2 or EMR clusters or equivalent with other providers, where there is no “notebook as control plane” garbage.
But when you add to that the fact that Databricks lacks full features for me to totally own every customized detail of my cluster environment (e.g. how can I run plain Python multiprocessing tasks with zero Spark in Databricks? How can I bring my own custom defined GPU container with custom compiled Tensorflow?) it makes the deal even worse.
Databricks is just another Spark / Hadoop style snake oil seller banking on capturing a bunch of data science teams before people realize that it’s a conceptually junk way to work.
As for the other tooling you mention, I’d almost always say to build it in house. For example, I don’t know of a single A/B testing provider that actually uses frequentist sequential testing to correctly avoid early stopping bias. You actually need real statisticians hired in-house to solve these problems, and the engineering work to set up an extensible A/B test as a service internal tool is not bad. (I’ve built Bayesian A/B test frameworks with teams of 2-3 engineers in 3 different companies). It’s just not cost effective to outsource it on the false hopes of not needing to hire your own in-house statistics experts. Just pony up the dough and hire them.
Re: When AWS, Azure, or GCP Becomes the Competition
#90Earlier quoted context omitted.
> While AWS has ruffled a bunch of feathers with their Elasticsearch and Kafka managed offerings Also DynamoDB (vs MongoDB)
Instead of DynamoDB, perhaps you meant DocumentDB which is AWS's managed MongoDB compatible database service.
Whoops, right! Hard to keep track of all those AWS services