Operations | Monitoring | ITSM | DevOps | Cloud

We’re excited to talk about all the innovative updates in the 25.7 release for Cove Data Protection!

Ever wondered what a vector database is and why it’s becoming so important in AI search? In this quick video, I’ll break down what a vector database is, how it works, and what you should consider when choosing one. About Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale. Elastic’s solutions for search, observability, and security are built on the Elastic Search AI Platform — the development platform used by thousands of companies, including more than 50% of the Fortune 500.

Join Ankit, Payal, and Leon as they explore recent articles about: the impact of unoptimized images on both your customers and carbon footprint; How generative AI can be confident but not correct; and the pros, cons, challenges, and benefits of disconnecting from our technology once in a while.

In a world where network traffic can suddenly spike, manually sifting through flow data is often a daunting task. Kentik AI's new Cause Analysis simplifies troubleshooting by quickly identifying changes in traffic by application, IP, ASN, or service. With just a few clicks, Cause Analysis helps you compare time periods, understand traffic shifts, and detect changes in your network. Kentik: Take the hard work out of running your network.

Reliability is a constant for every technology, whether it’s cloud, microservices, or AI. Full transcript:  Just a few years ago everybody was screaming about microservices, "That's the wave of the future," and now everybody's looking at AI. No matter what the change in technology hot topic is, your reliability should still be at the forefront of everything that you're doing.

Unlock smarter Git workflows with GitLens.

Data locality: AI models often rely on large datasets. Locating compute close to the data reduces transfer times and improves training performance. Latency sensitivity: Real-time AI applications, like recommendation systems or edge analytics, depend on low-latency environments. This can be more easily tuned in private or hybrid setups. Hardware specialization: Some AI workloads benefit from custom hardware like GPUs or TPUs. Private cloud allows more control over this, while public cloud offers broader access but less customization.

‎‎Subscribe. Fuel your curiosity. In this episode, we break down what we’ve learned from teams running AI at scale, and why on-premises infrastructure is making a strong comeback. We’re seeing a shift: performance, cost control, data sovereignty, and platform flexibility are driving conversations about on-prem strategies for AI. No one-size-fits-all answers, but if you’re building or scaling AI, this might help you think a few steps ahead.

In the July edition of the Kubernetes Monitoring Helm chart office hours, we discuss the version 3.2 release as well as the plan for upcoming features. Finally, we end with a packed Q&A full of great questions.

Get alerted on frontend issues the moment they happen — no setup headaches required. In this short demo, Elliot Kirk from Grafana Labs introduces out-of-the-box alerting for frontend observability. Whether you're tracking error counts or web vitals, this new feature makes it easy to stay ahead of performance issues. With just a few clicks, you can: Enable prebuilt alerts for your apps Visualize and edit alerts directly in the UI Customize thresholds and durations Set up notifications and stay in the loop Launch alerting with every new app setup.