Operations | Monitoring | ITSM | DevOps | Cloud

How AI-driven Anomaly Detection Fortifies Compliance in Multi-Cloud Infrastructures

In a multi-cloud environment, each cloud platform brings its unique tech stack to record events, manage services, set up configurations, manage user access and permissions, etc. While this allows you to leverage the best-of-breed services from different cloud vendors, the complexity of this setup makes it challenging to detect and respond to anomalies across clouds in real-time.

AI for Data Analytics: Unlocking the Power of Data Insights

According to McKinsey, 78% of organizations have implemented AI in at least one core function, with data analytics leading that transformation. AI no longer supports analytics from the sidelines; it now directs how data is queried, modeled, and delivered. It forecasts outcomes, detects anomalies, and reveals real-time insights, often before a dashboard loads. For SQL developers and DBAs, this marks a new phase in data work.

How to Use AI for MySQL: Optimizing Queries and Database Management

Imagine telling your database to get the best five customers by order from six months ago, and you get a well-optimized query instantly. Zero coding. No Googling syntax. Just results. Welcome to the future of database management with MySQL AI. MySQL Artificial Intelligence is a strategic solution transforming the traditional method of managing databases.

How Agentic AI is Reengineering Advertising Revenue Operations: Workflows to Workforce

Digital advertising is experiencing a shift similar to manufacturing's industrial revolution. AI is automating routine tasks, freeing up human teams for higher-level strategic work, moving us from manual campaign management to automated systems where humans design the strategy rather than execute every detail. This represents the biggest operational change since programmatic advertising began.

AI-Powered Monitoring with Checkly

Most monitoring tools weren't built for the AI-first world. By nature, traditional monitoring platforms force you out of your natural coding environment and trap you in clunky web interfaces, brittle configuration panels, and rigid APIs. And sadly, when monitoring providers do offer "AI features," it's usually a chatbot bolted onto their existing UI, being nothing more than a pale imitation of the AI tools you’re reading about every day on Hacker News. All this creates friction.

LangChain & LangGraph: The Frameworks Powering Production AI Agents

Your AI agent worked flawlessly in development, with fast responses, clean tool use, and nothing out of place. Then it hit production. A simple "What's our pricing?" query triggered six API calls, took 8 seconds, and returned the wrong answer. No errors. No stack traces. Unlike traditional systems, AI agents don't crash, they drift. They make poor decisions quietly, and your monitoring says everything's fine.

Introducing Netdata Insights

Subscribe to the channel → / @netdata Now in research preview: Netdata Insights The problem: Incident? You're jumping between dashboards, piecing together timelines. Reporting? You're copy-pasting charts and correlating trends by hand. The data’s there, but turning it into a narrative doesn’t scale. The solution: Netdata Insights. Synthesizes high-fidelity telemetry using the latest LLMs into AI-powered reports with natural-language explanations, visuals, and clear recommendations.

Netdata: The Fastest Path to Full Stack Observability. AI Powered.

Netdata is a real-time, high-performance and on-premises observability platform designed to monitor metrics and logs with unparalleled efficiency. Netdata requires zero-configuration to get started, and provides alerts, anomaly detection and AI assisted troubleshooting out of the box, providing a powerful and comprehensive infrastructure monitoring experience. Netdata is known for its distributed design. Instead of funneling all data into a few central databases like most traditional monitoring solutions, Netdata processes data at the edge, keeping it close to the source.