There’s a version of platform strategy that looks beautifully simple on a whiteboard. You pick a PaaS everyone trusts, pay the monthly bill, ship code, and sleep at night. Then one day the vendor quietly shifts focus to “sustainability engineering” and you realize the platform isn’t getting the love it used to. Sound familiar? That’s … Read more
Modern ML engineers and platform developers know that scaling machine learning workloads isn’t a matter of just adding GPUs. Optimizing ML system performance requires orchestrating compute, storage, and data pipelines to maximal efficiency under production constraints. As models grow more complex, what once was processed within a single rack now requires distributed GPU clusters, shared … Read more
Every machine learning engineer eventually encounters this challenge: a model that performs perfectly in a notebook often fails in production. The problem isn’t the algorithm itself; it’s everything surrounding it. In a lab environment, data is clean, schemas are consistent, and dependencies remain stable. However, in a production environment, data changes daily, infrastructure evolves, and … Read more
Catchpoint’s story grabs you from the start. The team launched in 2008, spun out of Google after the DoubleClick deal, so they built their platform on real‑world experience. At Cloud Field Day 22, they showcased how Internet Performance Monitoring (IPM) links every metric to actual user impact. What Is IPM? IPM goes beyond raw network … Read more