Operations | Monitoring | ITSM | DevOps | Cloud

Beyond the Hype Blog Part 2 - DeepSeek and Other AI Models

The recent introduction of the DeepSeek R1 (DeepSeek) Large Language Model (LLM) has shaken up the AI landscape, suggesting that new low-cost and open-sourced providers could enter the market. This disruption creates huge opportunities for service providers to drive innovation and for their vendors and suppliers to enhance or innovate in economically feasible ways.

It's time for a new approach: Edwin AI solves ITOps biggest challenges with agentic AI

For years, the term “AIOps” has been tossed around, but for IT teams, it hasn’t really brought the change it promised. Gartner coined the term, promising that machine learning and AI would forever change how we manage IT operations. Yet, the reality has been underwhelming. For most teams, traditional AIOps has amounted to little more than event management with a shiny new label.

The Role of Facial Recognition Cameras in Modern Surveillance AI Technologies

Facial recognition cameras have rapidly emerged as one of the most advanced tools in modern surveillance AI technologies, transforming security measures across industries. These intelligent systems integrate artificial intelligence with real-time data processing to identify individuals with remarkable accuracy, enhancing law enforcement, border control, and corporate security. Surveillance systems that leverage facial recognition cameras provide unmatched capabilities in monitoring and streamlining identity verification, making them an indispensable asset in today's security landscape.

AI Governance in 2025: A Full Perspective on Governance in Artificial Intelligence

In a world where artificial intelligence (AI) is leaping forward — growing at a CAGR of almost 36% from 2024 to 2030 — questions about governance and ethics with the use of AI are surfacing. As humans continue to develop AI systems, it is crucial to establish proper guidelines to ensure powerful technologies like generative AI and adaptive AI are used in a responsible manner.

How to avoid blowing the budget on Azure AI

So you had a great day playing with really awesome new tech, solving big business challenges, and feeling like you really nailed it. Then you wake up the next day to an alert from Azure telling you you've blown your monthly budget and its only the first week of the month. We've all been there... right? Using any cloud service comes with a cost, but for most services the budget risk is low. Cost calculated daily isn't a problem when usage is predictable, but not everything works like that.

How to Achieve Ethical Quality Assurance (QA) for Your Software Using Artificial Intelligence (AI)

As the use of artificial intelligence (AI) for software testing and quality assurance (QA) becomes increasingly prevalent, there are ethical considerations that must be addressed to ensure fairness, transparency, and accountability.

Three reliability best practices when using AI agents for coding

One of the biggest causes of outages and incidents is good old-fashioned human error. Despite all of our best intentions, we can still make mistakes, like forgetting to change defaults, making small typos, or leaving conflicting timeouts in the code. It’s why 27.8% of unplanned outages are caused by someone making a change to the environment. Fortunately, reliability testing can help you catch these errors before they cause outages.

Graylog Parsing Rules and AI Oh My!

In the log aggregation game, the biggest difficulty you face can be setting up parsing rules for your logs. To qualify this statement: simply getting log files into Graylog is easy. Graylog also has out-of-the-box parsing of a wide variety of common log sources, so if your logs fall into one of the many categories of log for which there is either a dedicated Input; a dedicated Illuminate component; or that uses a defined Syslog format; then yes, parsing logs is also easy.

Weaving AI into SIGNL4

Over the past two years, artificial intelligence (AI) has experienced remarkable growth, significantly influencing various sectors and daily life. In 2023, the release of advanced large language models (LLMs), such as OpenAI’s GPT-4 and Google DeepMind’s Gemini, marked a pivotal shift by enabling AI systems to process and generate diverse data types, including text, images, and audio.