Precompute and Cache Predictions
Blend batch and online to cut latency and cost.
Precompute and Cache Predictions is a free MLOps Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Blend the Best of Both
You can mix batch and online: precompute likely answers ahead of time, then serve them instantly at request time. Best of both worlds. 🧩
Precompute Defined
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.
Caching Defined
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.
The Cache Key
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.
cache.set(user_id, score)
score = cache.get(user_id)Hit Versus Miss
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.
score = cache.get(key)
if score is None:
score = model.predict(x)Fewer Live Model Calls
Every hit avoids a real prediction. That cuts latency and load, letting modest hardware serve far more traffic than calling the model each time.
Great for Repeats
Caching shines when the same inputs recur, like a popular product or a frequent user. Hot items get served straight from memory.
Staleness Returns
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.
cache.set(key, score, ttl=3600) # 1 hourInvalidate on Change
When the underlying data updates, drop the stale entry. Invalidation keeps the cache honest, though knowing exactly when to drop is tricky.
Precompute the Top Slice
You rarely need every answer cached. Precompute the most common cases and let the rare ones fall through to live inference.
When This Pattern Fits
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.
Quick Check
A request finds its answer already stored. What is that called?
Recap
Precompute and cache blends batch and online: store likely answers, serve hits instantly, and use TTLs to keep cached predictions fresh enough.
Frequently asked questions
Is the “Precompute and Cache Predictions” lesson free?
Yes — the full text of “Precompute and Cache Predictions” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.
What will I learn in “Precompute and Cache Predictions”?
Blend batch and online to cut latency and cost. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start MLOps Academy?
No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Precompute and Cache Predictions” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this MLOps Academy lesson?
Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Batch Scoring on a Schedule
- Real-Time Online Inference
- Latency, Throughput, and Cost Trade-offs
- Precompute and Cache Predictions