aml | Another Main Loop | SDK library

Using a survfit object, I can extract quantiles like this:

I understand that the := operator is a simple assignment that only gets assigned one and that = is a recursive assignment. But when they are combined I am confused and cannot figure out what is going on in this code block:

I’m having some issues trying to access a FileDataset created from two http URIs in an Azure ML Pipeline PythonScriptStep.

In the step, I’m only getting a single file named ['https%3A’] when doing an os.listdir() on my mount point. I would have expected two files, with their actual names instead. This happens both when sending the dataset as_upload and as_mount. Even happens when I send the dataset reference to the pipeline step and mount it directly from the step.

The dataset is registered in a notebook, the same notebook that creates and invokes the pipeline, as seen below:

To submit a parameter in an az ml cli run submit-pipeline command we use the syntax:

The ParallelRunStep Documentation suggests the following:

A named input Dataset (DatasetConsumptionConfig class)

I wrote this code to use ACPI for powering off computers from my program launched from 64-bit UEFI.

(sorry for long code, but I think all parts are necessary)

I was trying use H2OAutoML in Python to create a regression model, but I can't find how to pass 'weights_column'.

I try this two ways:

I'm using "tiles" to show content, clicking on a tile expands content. Within the expanding content, I'm using the left hand side for displaying text (spec-info) and for the right hand side I'm using (icon-percent) to display an icon. I'm happy with this outcome as the icon sitting at the right hand side is positioned well in comparison to the text. However when on smaller device sizes, (ipad, iphone) - the "split" view of using left and right would be reverted to full width with the icon scaling appropriately and displaying above the text. I'm familiar with media queries but not sure they're appropriate for this implementation? any ideas would be really appreciated! I've created a working fiddle here too: https://jsfiddle.net/6gsvjt4r/1/

I work in the fraud/AML (anti-money laundering) field, and we are exploring using a graph database to unearth hidden connections and links. I've read a fair amount abut graph databases lately (mostly neo4j, but I think the concepts are similar across different products?), and from what I can tell, they seem to be well-suited to this domain. The issue is that I'm having a hard time getting buy-in from tech management, as they seem to think that we can do the same things with our existing data reporting model, which is in Hadoop, and is essentially a data warehouse which has specific tables that provide many-to-many link tables between the core tables (I believe Kimball calls them 'bridge' tables?).

In a way, they seem to provide the same functionality as the relationship tables in a graph DB. Given that we have already constructed the link tablesin Hadoop, would a graph database provide any performance advantage for the kinds of things we may want to do (e.g. How is Customer A connected to Customer B), or have we largely negated any performance advantage of a graph DB by building all of the link tables?