Operations | Monitoring | ITSM | DevOps | Cloud

Ready to master anomaly detection? Join us for Part 2 of our "Applying AI/ML in Observability" series, where we do a deep dive into vmanomaly! In this live stream, Mathis and Marc will be joined by a very special guest: Fred Navruzov, the lead developer and mastermind behind VictoriaMetrics' vmanomaly. If you want to move beyond the basics and unlock the full potential of AI-driven observability, this is a session you can't afford to miss.

What if you had 5 errors, and instead of coming back to 5 issues in your feed, you got 5 pull requests fixing them? Seer is Sentry's new AI Debugging agent. it's able to stitch together all the context from your logs, stack traces, distributed tracing, codebase, and issues and figure out what broke, where, and how to fix it. Seer automation lets you automate that flow - and end up with a nice PR waiting for you to merge if it looks good. Check it out!

Storage is often underestimated: In infrastructure discussions, compute and networking get most of the attention, while storage is treated as secondary. For AI workloads, that can be a costly oversight. Data throughput for specialized hardware: AI infrastructure powered by GPUs can process massive volumes of data at unprecedented speeds. This puts immense pressure on the storage system to keep up. Scale-out performance: An on-prem, scale-out, software-defined storage setup allows you to meet high performance demands, grow capacity as needed, and stay in control of infrastructure costs.

Modular architecture: Decouple compute from storage so each can scale independently. This makes it easier to adapt to growing or shifting workloads over time. Future-ready hardware: Select GPUs and CPUs not just for current workloads but with an eye on scalability, including support for newer accelerator types. Scalable design: Ensure the system allows seamless addition of compute nodes or storage without a full redesign.

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

In this episode, we take you behind the scenes of Argo CD’s architecture to explore its core components, how they interact, and the design principles that power it.

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.