Operations | Monitoring | ITSM | DevOps | Cloud

Sift is a machine-learning-powered diagnostic feature in Grafana Cloud that SREs and DevOps teams can use to automate routine parts of incident investigation, such as searching for new errors in logs, surfacing recent deployments, or identifying overloaded Kubernetes nodes. We want Sift to springboard you into an investigation, so useful context is already there by the time you see an alert or declare an incident.

Artificial Intelligence (AI) is increasingly becoming integral in the tech landscape, transforming virtual assistants and self-remediating software to enhance user experiences and propel IT automation in the workplace. Most of the tech workforce embraces AI, recognizing the potential advancements and business improvements it brings. In the workplace, smart technology aims to support and elevate the employee experience, not to replace human workers.

Cloud infrastructure scaling can assist in rightsizing cloud resources in various ways. When discussions occur regarding scaling, its context can often refer to adding resources. However, it can also refer to removing resources. Preparing for how your cloud infrastructure scales begins with thought-out planning, design, and management of resource and tool allocations. This can save your organization time and costs that could otherwise negatively affect your employees, customers, and company.

In an era marked by the convergence of Big Data, hybrid cloud use, and the rise of machine learning and artificial intelligence, organisations across the Asia-Pacific (APAC) region find themselves at a critical juncture. The ability to collect, manage, and leverage data effectively is now a determining factor for competitive advantage – and will become more competitive.

Despite changes in technology, culture, economics, or virtually any other factor imaginable, the adage ‘time is money’ remains relevant. When it comes to data analysis, the faster you can conduct analysis, the better. However, increasing data volumes across the board make it challenging to analyze and act on data in a timely manner.

Imagine a world where IT systems hum along, proactively detecting and resolving issues before they turn into full-blown outages. No frantic fire drills, no late-night heroics, just seamless self-healing powered by automation. It’s the siren song of self-healing IT systems, beckoning every enterprise ITOps team. Despite the allure of streamlined incident response workflows, many attempts at IT automation sink before they can swim.

If you’ve ever watched one of those rehabilitating a business show like Kitchen Nightmares, you know that the advice is often the same, “Don’t try to be everything to everyone. Pick what you’re good at and stick with it.” Essentially, the message is to pick your niche and be the best within it.

DDoS attacks disrupt services and damage reputations, with motivations ranging from political to personal. These attacks can also mask more severe security breaches, so early detection and mitigation are crucial. Learn how Kentik provides a solution by analyzing enriched NetFlow data to identify and mitigate DDoS threats.

Artificial intelligence tools are everywhere, and it’s no mystery why. They can carry out a huge variety of tasks and find the solutions to many everyday problems. But these apps are only as good as their AI search algorithm. In simple terms, an AI search algorithm is the decision-making formula an AI tool uses to find the optimal solution to your specific problem. Search algorithms may make trade-offs between speed, relevance, or another weighted factor.