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In some projects, the build script is playing the role of Cinderella. The team focuses its main effort on code development. And the build process itself could be handled by people who are far from development (for example, those responsible for operation or deployment). If the build script works somehow, then everyone prefers not to touch it, and no one ever is thinking about optimization. However, in large heterogeneous projects, the build process could be quite complex, and it is possible to approach it as an independent project.If you treat the build script as a secondary unimportant project, then the result will be an indigestible imperative script, the support of which will be rather difficult.

In this note we will take look at the criteria by which we chose the toolkit, and in the next one — how we use this toolkit. (There is also a Russian version.)

Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

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”).

Distributed AI scenarios “for the masses”

We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).

By the beginning of 2021, Qrator Labs filtering network expands to 14 scrubbing centers and a total of 3 Tbps filtering bandwidth capacity, with the San Paolo scrubbing facility fully operational in early 2021;

New partner services fully integrated into Qrator Labs infrastructure and customer dashboard throughout 2020: SolidWall WAF and RuGeeks CDN;

Upgraded filtering logic allows Qrator Labs to serve even bigger infrastructures with full-scale cybersecurity protection and DDoS attacks mitigation;

The newest AMD processors are now widely used by Qrator Labs in packet processing.

DDoS attacks were on the rise during 2020, with the most relentless attacks described as short and overwhelmingly intensive.

However, BGP incidents were an area where it was evident that some change was and still is needed, as there was a significant amount of devastating hijacks and route leaks.

In 2020, we began providing our services in Singapore under a new partnership and opened a new scrubbing center in Dubai, where our fully functioning branch is staffed by the best professionals to serve local customers.

The step has been made. Not sure where to, but for sure from the point of no return. Keep calm and keep walking. It is about time to look around and understand the smelly and slippery route before you. And what are those noisy creatures swarming around our fishy “innovative” design we called Mandelbrot blueprint? You don't get a buzzing noise like that, just buzzing and buzzing, without its meaning something.

Architect. This word sounds so mysterious. So mysterious that to understand it you are almost forced to add something. Like “System Architect” or “Program Architect”. Such an addition does not make it clearer, but for sure adds weight to the title. Now you know – that’s some serious guy! I prefer to make undoubtful and around 10 years ago added to my email signature “Enterprise Architect of Information Systems”. It’s a powerful perk. Like “Chosen One”. With architects it is always the matter of naming, you know. Maybe that is why the only way to become an architect is to be named as one by others. Like with vampires. One of them has to byte you! That is probably the easiest way to earn the title as there is no degree or school to grant you one. And if there’s a troubling title, somebody’s making a trouble, and the only reason for making a trouble that I know of is because you’re an Enterprise. Huge old and complex multinational corporation. Like a one-legged pirate. Strong and scary, but not a good runner. You own your ship, you had good days, you have some gold, you need new ways.

To get to new treasures and avoid losing the second leg to piranha regulators and local business sharks swarming waters near every enterprise ship – every pirate has a map. A map is a list of major features and requirements in desired order and priority.

The DHT system has existed for many years now, and torrents along with it, which we successfully use to get any information we want.

Together with this system, there are commands to interact with it. There are not many of them, but only two are needed to create a decentralized database: put and get.

What is HDB++?

This is a TANGO archiving system, allows you to save data received from devices in the TANGO system.

Working with Linux will be described here (TangoBox 9.3 on base Ubuntu 18.04), this is a ready-made system where everything is configured.

What is the article about?

• System architecture.
• How to set up archiving.

It took me ~ 2 weeks to understand the architecture and write my own scripts for python for this case.

What is it for?

Allows you to store the history of the readings of your equipment.

• You don't need to think about how to store data in the database.
• You just need to specify which attributes to archive from which equipment.

Tested on the following configuration:
Server: ubuntu 20.04
Clients: ubuntu 16.04, 18.04, 20.04

It doesn’t require a lot of software to create it.

Let's once again improve Ansible. Well, this won't work without getting into sources.

Technology is as adaptable and compatible as mankind; it finds its way through problems and situations. 2020 was one such package of uncertain events that forced businesses to adapt to digital transformation, even to an extent where many companies started to consider the remote work culture to be a beneficiary long-term model. Technological advancements like Hyper automation, AI Security, and Distributed cloud showed how any people-centric idea could rule the digital era. The past year clearly showed the boundless possibilities through which technology can survive or reinvent itself. With all those learnings let's deep-dive and focus on some of the top technology trends to watch out for in 2021.

Let's say you use GitHub, write code, and do other fun stuff. You also use a static analyzer to enhance your work quality and optimize the timing. Once you come up with an idea - why not view the errors that the analyzer gave right in GitHub? Yeah, and also it would be great if it looked nice. So, what should you do? The answer is very simple. SARIF is right for you. This article will cover what SARIF is and how to set it up. Enjoy the reading!

Speech analytics is the process of analysing recorded speech, such as phone calls, to gather customer information to improve communication and future customer interaction. Speech analytics as a technology has been evolving especially rapidly over the last few years. It gives the ability to structure and analyse previously lost streams of insight-rich data, such as phone conversations. Empowered with this technology, operations can gather incredibly valuable business intelligence to drive call delivery performance improvements. It’s smart in that it automatically identifies focus areas in which customer service or sales teams may need additional call training which then, in turn, improves the call’s successful outcome. Speech analytics, as a process, can isolate buzzwords and phrases used most frequently within a given time period, plus indicate usage is trending up or down. This data is highly useful to call managers to spot changes in consumer behaviour so that action can be taken to improve customer satisfaction.

Zadarma is a leading global VoIP provider and offers a smart speech analytics feature as part of their incredibly easy to use telecommunications offering. The tool is free as part of the wider PBX phone system bundles, included in the free recognition minutes. Zadarma’s analytics feature allows data access to every internal or external call conversation. The benefits of speech analytics include: