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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.

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.

Empowering DevOps Teams: Overcoming IT Complexity with Advanced AI + Automation

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.

Operational excellence in the age of AI and Automation

The future of operations is here with PagerDuty's groundbreaking AI and automation innovations. Learn how PagerDuty AI agents, powered by PagerDuty Advance, and new use cases like security incident management and LLMOps can help your organization achieve operational excellence to reduce cost, mitigate the risk of outages, and accelerate innovation.

The One Where We Meet Cribl Copilot

We’re kicking off our new live weekly product demo series—streaming on YouTube, X, and LinkedIn! Each week, we’ll dive into the latest features and hidden gems from the Cribl Suite of tools to help you unlock the full potential of your telemetry data. For our first session, we’re thrilled to welcome Nikhil Mungel, the visionary behind Cribl Copilot. This AI-powered assistant is designed to: Instantly surface answers from the documentation Build pipelines with just a simple request.

How to make your AI-as-a-Service more resilient

When you think about “AI reliability,” what comes to mind? If you’re like most people, you’re probably thinking of generative AI model accuracy, like responses from ChatGPT, Stable Diffusion, and Sora. While this is certainly important, there’s an even more fundamental type of reliability: the reliability of the infrastructure that your AI models and applications are running on. AI infrastructure is complex, distributed, and automated, making it highly susceptible to failure.

How AI is impacting Africa's connectivity landscape

Artificial Intelligence (AI) is reshaping industries worldwide, and Sub-Saharan Africa is no exception. Across the region, governments, businesses, and start-ups are recognising the potential of AI to drive economic growth, improve efficiencies, and enhance decision-making. Yet, as AI adoption accelerates, so does the demand for robust digital infrastructure, including high-performance computing, data centres, and connectivity.

Kubernetes for AI Workloads

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.