#Cloud

Challenges of real-time AI/ML computations

We will start from the examples that we faced as Data Science practice at InterSystems:

• A “high-load” customer portal is integrated with an online recommendation system. The plan is to reconfigure promo campaigns at the level of the entire retail network (we will assume that instead of a “flat” promo campaign master there will be used a “segment-tactic” matrix). What will happen to the recommender mechanisms? What will happen to data feeds and updates into the recommender mechanisms (the volume of input data having increased 25000 times)? What will happen to recommendation rule generation setup (the need to reduce 1000 times the recommendation rule filtering threshold due to a thousandfold increase of the volume and “assortment” of the rules generated)?
• An equipment health monitoring system uses “manual” data sample feeds. Now it is connected to a SCADA system that transmits thousands of process parameter readings each second. What will happen to the monitoring system (will it be able to handle equipment health monitoring on a second-by-second basis)? What will happen once the input data receives a new bloc of several hundreds of columns with data sensor readings recently implemented in the SCADA system (will it be necessary, and for how long, to shut down the monitoring system to integrate the new sensor data in the analysis)?
• A complex of AI/ML mechanisms (recommendation, monitoring, forecasting) depend on each other’s results. How many man-hours will it take every month to adapt those AI/ML mechanisms’ functioning to changes in the input data? What is the overall “delay” in supporting business decision making by the AI/ML mechanisms (the refresh frequency of supporting information against the feed frequency of new input data)?

If you're looking for an introduction to Kubernetes as a technology, and a little teaser about the InterSystems Kubernetes Operator, check out the recently released Kubernetes Overview video. This video will introduce you to the functionality and use cases of Kubernetes, and explain a bit about how the InterSystems Kubernetes Operator makes it beneficial to use Kubernetes with InterSystems IRIS.

https://www.youtube.com/embed/d2_1aptaL4E
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All source code to the article is available at: https://github.com/antonum/ha-iris-k8s

Why SSH ?

If you do not have direct access to the server that runs your IRIS Docker container
you still may require access to the container outside "iris session" or "WebTerminal".
With an SSH terminal (PuTTY, KiTTY,.. ) you get access inside Docker, and then, depending
on your needs you run "iris session iris" or display/manipulate files directly.

Note:

Hi Developers,

Please welcome the new video specially recorded for Developer Tools programming contest:

⏯ Deploying InterSystems IRIS docker solutions to GKE cloud in 5 minutes

https://www.youtube.com/embed/-8XaMxsauio
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What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

AWS launched their first generation of Amazon EC2 A1 instances last year, powered by Arm-based AWS Graviton processors.

Episode 16 of Data Points features a conversation with Jeff Fried — Director of Product Management for Data Platforms at InterSystems — about what it means for InterSystems to have been named a visionary in Gartner's first-ever magic quadrant for Cloud Database Management Systems.

https://5e18edf067eb59-03854285.castos.com/player/344317
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Hi team.

Does anyone has any idea on how to send an encrypted / secure Print from IRIS ( which is hosted on AWS ) to a printer ( which is an on-premise device ).

Thanks

Paras

Hi Community!

We are pleased to invite you to the upcoming webinar in Spanish: "Improving Kubernetes performance", on December 9, at 4:00 PM (CET).

Hi Community!

Please welcome the new video on InterSystems Developers YouTube:

⏯ Get InterSystems IRIS from the Docker Store

https://www.youtube.com/embed/guTiFXELt8E
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ML Toolkit for InterSystems IRIS, besides Python/R/Julia, allows orchestrating cloud-based advanced analytics services, such as Microsoft Azure Data Factory and Machine Learning:

Hi All,

With this article, I would like to show you how easily and dynamically System Alerting and Monitoring (or SAM for short) can be configured. The use case could be that of a fast and agile CI/CD provisioning pipeline where you want to run your unit-tests but also stress-tests and you would want to quickly be able to see if those tests are successful or how they are stressing the systems and your application (the InterSystems IRIS backend SAM API is extendable for your APM implementation).

Imagine you want to see what InterSystems can give you in terms of data analytics. You studied the theory and now you want some practice. Fortunately, InterSystems provides a project that contains some good examples: Samples BI. Start with the README file, skipping anything associated with Docker, and go straight to the step-by-step installation. Launch a virtual instance, install IRIS there, follow the instructions for installing Samples BI, and then impress the boss with beautiful charts and tables. So far so good.

Inevitably, though, you’ll need to make changes.