Aleksandr Surkov

Initially, I intended to write an article about the practice of using yaml templates in Azure DevOps to encapsulate repeatable steps in pipelines. But during the writing process, I realized that boring references to documentation and simple examples of yaml files would not be so interesting without tying them to some scenario. Thus, a short, purely technical article turned into a large text dedicated to several topics, each of which is interesting in itself. As a result, I decided to turn this text into a mini-series of articles. Here comes the second part, dedicated to using YAML templates in Azure DevOps, focusing specifically on passing parameters to and from the templates.

Initially, I intended to write an article about the practice of using yaml templates in Azure DevOps to encapsulate repeatable steps in pipelines. But during the writing process, I realized that boring references to documentation and simple examples of yaml files would not be so interesting without tying them to some scenario. Thus, a short, purely technical article turned into a large text dedicated to several topics, each of which is interesting in itself. As a result, I decided to turn this text into a mini-series of articles. Welcome to the first part, dedicated to automated testing, where I will give a brief overview of software testing approach and describe a scenario that we will automate using yaml pipelines with templates in the next article.

Today, anyone heard the words artificial intelligence, neural networks, ChatGPT . The topic is so relevant and important that it is simply impossible to pass it by. In this article, I will discuss a little about what modern artificial intelligence is and tell you about the services that you can use in Azure to work with neural networks.

Not so far ago, I ran into a situation where one of our Azure DevOps deployment agents disappeared from the network. Everything was fine with the machine, the Internet worked on it, the firewall settings and the Network Security Group were also good, but Azure DevOps stopped seeing the agent. What happened and how did we fix it? Welcome to the article.

Last time, we figured out how the cloud works at the lowest level. And now I suggest you look inside the cloud from the other side. I think that everyone knows what serverless functions are (those who do not know, go to the article anyway - you will find out there :). An example of such serverless technologies is Azure Functions, one of the most powerful tools that allows you to easily and elegantly solve many computational problems. So let's figure out how serverless functions work and there really are no servers there...

In continuation of my thoughts about the internal structure of clouds, today I would like to reflect on how it is generally they work inside from the point of view of IT engineering. Let's try to think of what layers and blocks Microsoft Azure can consist of.

After discussing how data centers work, I would like to add a few words about ensuring reliability and security within teams working in cloud vendors. I have some experience working for a cloud provider, and below I will list a few points that are mandatory for any cloud vendor's employees to follow.

Cloud technologies became a big and important part of our live and in most cases, modern IT engineers can’t imagine their work without interacting with cloud services. But, clouds don't work by themselves somewhere in the sky. Somewhere on our planet, there are data centers with a lot of servers that host all SaaS, PaaS and IaaS services we use. In this article, I will talk a little about data centers - “whales” on which clouds stand.