In a time when technology permeates every aspect of our lives, the advancement of AI emotion recognition presents both opportunities and difficulties. Tech developers encounter a great deal of difficulty when negotiating the wide cultural variations in emotional expression and perception. It is essential to comprehend and take into account these differences to develop inclusive and efficient emotion recognition systems.

The business world is in constant flux, and the way we handle Incident Management (IM) needs to evolve alongside it. Incidents come in all priorities and urgencies, and while some can be addressed with any planning, others are simply unpredictable. That's why businesses can't afford to be caught off guard. The potential consequences of such incidents for businesses have never been greater. A single event can disrupt operations, damage reputations, and result in significant financial losses.

It’s one thing to say that reliability is a priority for your organization, and a whole other thing to make actual, demonstrable improvements in the availability of your applications. Sadly, it’s common for organizations to invest time, money, and effort into improving reliability only to barely nudge the needle on incidents and downtime. But there are hundreds of companies successfully improving their reliability posture—and doing it at enterprise scale.

Modern, distributed application architectures pose particular challenges when it comes to coordinating incident management. DevOps, SREs, and security teams—often spread out across separate locations and time zones, and equipped with limited knowledge of each other’s services—must work quickly to collaboratively triage, troubleshoot, and mitigate customer impact.

BGP Flowspec is one of the critical technologies gaining traction in DDoS mitigation strategies. In this article, Justin Ryburn outlines the adoption rates and best practices of this emerging and effective DDoS mitigation tool.

Here at Mezmo, we see the purpose of a telemetry pipeline is to help ingest, profile, transform, and route data to control costs and drive actionability. There are many ways to do that as we’ve previously discussed in our blogs, but today I’m going to talk about real-time alerting on data in motion, yes - on streaming data, before it reaches its destination.

AI-driven processes, including machine learning models and data processing, require significant computational resources which can lead to increased energy consumption and heightened operational costs. The complexity of these workloads, which often involve real-time data analysis and continuous model training, exacerbates the need for robust data center management.

IT teams need immediate and automatic access to machine data and institutional knowledge to move faster and make the right decisions. And they need context to identify incidents and understand how to resolve them. AIOps enables this by transforming noisy and fragmented operations data into actionable insights. This is the foundation of full-context operations. Full-context operations combines observability and other machine-generated data with historical, expert, and institutional knowledge.

When it comes to IT automation, the choice between a service orchestration and automation platform like Resolve Actions or building your own automation engine or workflow tool is a pivotal decision. While each option presents its own set of strengths and considerations, Resolve Actions emerges as a powerful solution for organizations looking to streamline their automation efforts. Here’s why.

Observability isn’t new. But organizations are struggling to adopt mature observability practices, and the impact on business is palpable. Organizations are seeing the value of observability for their applications and infrastructure—the results of our 2024 Observability Pulse survey of 500 global IT professionals reflects that across the board.