This is supported by MinIO, but not "as a service." Essentially you run MinIO everywhere (AWS, GCP, Azure, IBM, on-prem, OpenShift, Tanzu etc). In the public clouds you can either roll your own or use the marketplace offerings.
In effect, you are choosing MinIO object storage over the "stock" object storage (which is incompatible with the other clouds).
You can use MinIO's ILM policies to replicate, tier, etc.
You still pay for compute, network + drive but then pay MinIO vs. S3/Blob. There will be no egress fees.
Many companies that do this look at MinIO for object storage. Given they run in AWS, GCP and Azure, they will minimize or eliminate your application rewrites. They are cloud-native by design and very fast.
The key here is that TDA is packaged into an application that is designed explicitly for use by practitioners. All of the underlying math (and you know there is lots of it in TDA) is abstracted. What is shown is the groups and the atomic level explains (this group is here for these reasons e.g. they received albuterol upon admittance). Your instinct is correct, but that is what is interesting about this case - the hospital, without a single data scientist, was able to to achieve this with only slick SQL skills and engaged doctors.
Hi infinite8s, to get additional information on how that chart was made, you can to go https://www.mapd.com/product/ scroll down to the bar chart, and click “See Details” under the chart. Shows the machines used, queries, and the source data set and size. Note that the machine configurations used to generate the chart were normalized for equivalent cost on AWS, i.e. the chart is hardware-dollar normalized.
Thanks for the info. The tl;dr is "It's fantastic to see that I've been able to use a machine that costs 1/10th of the one used in the 8 x Tesla K80s benchmark but still have queries running within 33% of the previous performances witnessed."
However, I'm suspicious of the numbers in those articles since the author lists only 4 data points in each trial and doesn't mention the stdev in his measurements. One of his measurements was .964 vs .891 so it looks like the Titan Xs were 90% as fast as K80s if the numbers can be trusted.
thanks, but at 5 bucks an hour for an entry-level instance (single 12GB GPU) I'm looking at 120 bucks a day if I don't want to constantly re-upload my dataset into MapD (a very slow operation judging by Mark Litwintschik's posts linked by you). That's a very very high price for such a modest hardware configuration, not to mention the more credible one which goes for an eye-watering 30 bucks an hour ie not much change from a grand a day. Not for us startup folk, clearly.
I have to say it seems your pricing for such a new entrant and before having built share, is bound to attract very stiff newcomer competition. "Interesting" business model.
MapD has a persistent store and normally customers would keep that on an EBS volume, so they don't have to reload their data every time they spin up an AWS instance.
Look at the coloring around the rides near bridges. People take the subway down to the closest point and then take a cab home. The hybrid trip is both pocketbook friendly and probably faster.
