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jtsymondson Ask HN: Does anyone know why Docker pull stats have plummeted in last 30 days?

So helpful. That makes total sense.

jtsymondson Scared tech workers are scrambling to reinvent themselves as AI experts

Uhh, they were using AI moniker in 2019. They just circled back to it this year:

https://web.archive.org/web/20190428005007/https://min.io/

jtsymondson Ask HN: Is there any multi-cloud storage aggregator company?

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.

jtsymondson We decided to move 90% of our workload from the cloud to on-prem infrastructure

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.

jtsymondson How a tiny hospital used AI to lower costs and improve patient outcomes

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.

Screenshots for the app and videos can be found here: https://www.ayasdi.com/solutions/clinical-variation-manageme...

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

Twrrim,

Slightly different. We have appended all of the data from Factual as well. This includes the location of every business in NYC.

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

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.

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

Blooper Fixed :)

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

Yep, sorry about that:

Here is the Titan X link http://bit.ly/2e6C3Gg

Here is the K80 link: http://bit.ly/2eiIwvp

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

Hi SXP, thanks for your comment. You might want to check out Mark Litwintschik's posts (independent blogger who has benchmarked this dataset across many different databases) for performance on GeForce GTX TITAN X's. 4 x GeForce GTX TITAN X: http://tech.marksblogg.com/billion-nyc-taxi-rides-nvidia-tit.... 8 x K80s: http://tech.marksblogg.com/billion-nyc-taxi-rides-nvidia-tes.... He has additional posts on MapD on Pascal Titan X's and AWS as well. In full disclosure I work at MapD...

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

There is not an open source version as yet, but you can spin up these instances on an hourly basis on AWS https://aws.amazon.com/marketplace/pp/B01M0ZY2OV?qid=1475606... and on IBM Softlayer.

jtsymondson Using GPUs to Speed Through the 1.2B Record Taxi Dataset

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.