Operations | Monitoring | ITSM | DevOps | Cloud

Alerts tell you something broke, but not why. So you're stuck digging through logs and trace IDs. Checkly monitors your app from the outside, like a real user. And Rocky, the Checkly agent, automatically pulls the right context to provide a root cause analysis for any failed check.

Can AI actually improve infrastructure operations? Without sacrificing control? In this webinar, see how teams use Puppet AI to understand infrastructure with natural language, reduce operational effort, and move from insight to action faster—all within trusted automation workflows. Watch a live demo of detecting and mitigating a real-world vulnerability, and learn how context-aware AI helps teams scale safely with built-in governance.

Learn why manual patch management creates unnecessary risk for IT teams and how automated patch management helps organizations improve security, compliance, and operational efficiency. Discover how automation eliminates repetitive tasks, reduces human error, prioritizes critical vulnerabilities, and accelerates patch deployment across the entire IT environment.

I tested the new Anthropic model Fable 5 truly is using data, not vibes. Why does it feel faster? Does it actually cost double? Is it better at coding?

Enterprise service management (ESM) applies the proven model of IT service management, catalogs, workflows, self-service, and SLAs, to the whole business: HR, facilities, finance, and more. Here is what it is and how it works. What is enterprise service management, and how is it different from ITSM? In this explainer we define ESM, show how it works across departments, clarify how it builds on IT service management, and cover the mistake most teams make: copying IT ticket forms instead of orchestrating work across teams.

In this video, our engineering team takes you through a full end-to-end Kubeflow implementation, step by step – from data exploration to production inference. Follow the journey of a house price prediction use case and see how modern MLOps components work together: Kubeflow architectures and starter repositories Notebook-based development workflows Data exploration and model development MLflow for experiment tracking Katib for hyperparameter optimization Kubeflow Pipelines for automated preprocessing and training KServe for scalable model inference.