Operations | Monitoring | ITSM | DevOps | Cloud

‎‎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.

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.

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.

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.