คำนวณผลพยากรณ์ล่วงหน้าและแคชไว้

You can mix batch and online: precompute likely answers ahead of time, then serve them instantly at request time. Best of both worlds. 🧩

To precompute is to run predictions before they are asked for, in a batch job, and stash them so the live path just looks them up.

A cache is fast storage holding ready answers. On a request you check the cache first and skip the model when the answer is already there.

You store each prediction under a key, often the input or an id. The same input maps to the same key, so repeats are served in microseconds.

A cache hit means the answer was found and returned fast. A miss means you fall back to running the model live, then store the result.

Every hit avoids a real prediction. That cuts latency and load, letting modest hardware serve far more traffic than calling the model each time.

Caching shines when the same inputs recur, like a popular product or a frequent user. Hot items get served straight from memory.

A cached score can age as data changes. You manage this with a TTL, a time-to-live that expires entries so they get refreshed.

When the underlying data updates, drop the stale entry. Invalidation keeps the cache honest, though knowing exactly when to drop is tricky.

You rarely need every answer cached. Precompute the most common cases and let the rare ones fall through to live inference.

Use precompute and cache when inputs repeat and slight staleness is fine. It buys speed and savings without a fully live model behind each call.

A request finds its answer already stored. What is that called?

Precompute and cache blends batch and online: store likely answers, serve hits instantly, and use TTLs to keep cached predictions fresh enough.