Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.

As IT environments become more complex, larger, and inundated with data, DevOps teams encounter significant obstacles that make efficient operations more challenging. The heightened complexity can create difficulties in maintaining visibility and control across hybrid IT ecosystems. Additionally, the substantial volume of data generated can overwhelm resource-constrained DevOps teams, making it difficult to extract valuable insights and make informed decisions.

Kubernetes has been facilitating container orchestration for around a decade for both stateful and stateless application workloads. With the recent rise of AI and the advent of tools like Kubeflow and Argo Workflows, Kubernetes is also becoming a first-class citizen when it comes to running AI workloads. When you are training a model on K8s, you may be tweaking many parameters and have to test each of them one by one.