Operations | Monitoring | ITSM | DevOps | Cloud

Selector is excited to give a sneak peek into new features to be included in our forthcoming Spring Release. This release highlights key innovations focusing on integrated generative AI (GenAI) to enable guided troubleshooting and automated incident remediation. It also includes enhancements to several existing features, such as root cause analysis, native monitoring, and observability capabilities.

In all my blog posts so far, I have been providing suggestions on how to expedite your AI adoption journey. I believe it is now time to describe what we are doing here at Digitate: Helping enterprises to become a ticketless business. Even though this is the same adoption journey that I have been describing, it has a different perspective; I’m turning my focus “lens” from outward to inward.

For a long time, AI was almost exclusively the plaything of science fiction writers, where humans push technology too far, to the point it comes alive and — as Hollywood would have us believe — starts to wreak havoc. Cheery stuff! However, in recent years, we’ve seen an explosion of AI and machine learning technology that, so far, has shown us a fun side with people using AI for creating, planning, and ideating in a big way.

In recent years, artificial intelligence (AI) has emerged as a pivotal technology to advance cloud computing, driving innovation and greater efficiency. Among the myriad of AI services available, the Azure OpenAI Service, born from the collaboration between Microsoft and OpenAI, stands out for its robust capabilities and seamless integration with cloud environments.

Every day the world is becoming increasingly powered by artificial intelligence. In fact, you’d struggle to find tech companies that have not announced AI integrations into their tech stack in one way or another. Cynics might say this is a passing phase, but the reason AI is so popular is that it’s a versatile set of capabilities that can help solve a lot of problems.

If you’re working with AI, you’re working with data. From numerical data to videos or images, regardless of your industry or use case, every AI project depends on data in some form. The question is: how can you efficiently store that data and use it when building your models? One answer is PostgreSQL, a proven and well-loved database that, thanks to recent developments, has become a strong choice to support AI.