#Cloud

Most of us are more or less familiar with Docker. Those who use it like it for the way it lets us easily deploy almost any application, play with it, break something and then restore the application with a simple restart of the Docker container.

Apache Superset is a modern data exploration and data visualization platform. Superset can replace or augment proprietary business intelligence tools for many teams. Superset integrates well with a variety of data sources.

And now it is possible to use with InterSystems IRIS as well.

An online demo is available and it uses IRIS Cloud SQL as a data source.

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).

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)?

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”).

If you're running IRIS in a mirrored configuration for HA in Azure, the question of providing a Mirror VIP (Virtual IP) becomes relevant. Virtual IP offers a way for downstream systems to interact with IRIS using one IP address. Even when a failover happens, downstream systems can reconnect to the same IP address and continue working.

The main issue, when deploying to Azure, is that an IRIS VIP has a requirement of IRIS being essentially a network admin, per the docs.

To get HA, IRIS mirror members must be deployed to different availability zones in one subnet (which is possible in Azure as subnets can span several zones). One of the solutions might be load balancers, but they, of course, cost extra, and you need to administrate them.

In this article, I would like to provide a way to configure a Mirror VIP without the using Load Balancers suggested in most other Azure reference architectures.

Want a commercial grade FHIR® Implementation included in your micro service ecosystem and barely have enough time to fill out your Health plan elections?

Your may not realize it, but your InterSystems Login Account can be used to access a very wide array of InterSystems services to help you learn and use InterSystems IRIS and other InterSystems technologies more effectively. Continue reading to learn more about how to unlock new technical knowledge and tools using your InterSystems Login account. Also - after reading, please participate in the Poll at the bottom, so we can see how this article was useful to you!

Like hardware hosts, virtual hosts in public and private clouds can develop resource bottlenecks as workloads increase. If you are using and managing InterSystems IRIS instances deployed in public or private clouds, you may have encountered a situation in which addressing performance or other issues requires increasing the capacity of an instance's host (that is, vertically scaling).

With the world (as well as our own technology) moving to the cloud at such a fast pace it is easy (at least for myself) to get caught up in the little details. One thing I, and some clients of ours, had run into a couple of times was the necessity to specify the version of the images one plans to use with the IKO.

Summary

A Quick start to include InterSystems IRIS Tables in Data Build Tool using Python.

It uses the sqlalchemy plugin with sqlalchemy-iris which enables the iris strategy for duckdb as a source for a dbt project.

Function as a service (FaaS) is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage application functionalities without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. Building an application following this model is one way of achieving a "serverless" architecture, and is typically used when building microservices applications.

Wikipedia

FaaS is an extremely popular approach to running workloads in the cloud, allowing developers to focus on writing code.

This article will show you how to deploy InterSystems IRIS methods in a FaaS way.

Hi Community,
In this article, I will introduce my application iris-mlm-explainer