Reliable delivery starts with the rules that define what may be cached, for whom, and for how long. This collection moves from the basic CDN request path to experiments that use demand signals. Read the foundations before using a forecast to change a production policy.
For a new implementation, write down the origin, object families, freshness needs, and private-response boundaries. For an existing system, identify one measurable problem such as repeated origin work or a known release spike. A machine-learning experiment should follow those decisions, with a baseline and a clear way to stop it.
For the broader architecture, explore CDN + ML. Follow the articles below as a reading path, or use the guide directory to connect this topic with the rest of the stack.
Predictive caching moves a possible future miss ahead of the user's request. Learn how to rank eligible objects, set a warming budget, handle stale forecasts, and test whether earlier transfers improve delivery enough to justify their cost.
Machine learning can help a delivery system choose what to cache, where to send traffic, and when to investigate unusual behavior. Start with a narrow decision, a clear baseline, and boundaries that remain dependable when predictions fail.