Operations | Monitoring | ITSM | DevOps | Cloud

Insights to keep AI applications reliable

AI has become a massive investment for companies. Engineering teams across industries are integrating AI into their products, whether it’s through homegrown, self-managed models or third-party model integrations. But no matter how much AI shifts the user experience, it’s still an application, which means your engineering team still needs to operate it and keep it reliable. At the same time, AI applications add complexity and complications that require a shift in your approach.

How to test your systems for scalability and redundancy with fault injection

Part of the Gremlin Office Hours series: A monthly deep dive with Gremlin experts. Do you know if your services can tolerate losing a node? What about an entire availability zone? Or a region? Large-scale outages aren’t unheard of. When you’re running critical services, it’s vital that those services can keep running even if an AZ or region fails. In addition to failing over, these services also need to scale quickly so traffic shifts don’t overwhelm your systems. How do you prove that a service is both scalable and redundant? The answer is with Fault Injection.

How to be prepared for cloud provider outages

GCP’s recent outage on June 12th was a reminder of just how interconnected modern architectures are. The 2 hour and 28 minute outage affected dozens of companies and spanned 80+ Google services and products. But what was really illuminating was just how far the outage spread due to hidden dependency risks. Many companies that don’t run on GCP were startled to find their services suddenly affected because they had dependencies or depended on vendors that did use GCP.