Batch Scoring on a Schedule
Predict over a whole dataset and store results.
Batch Scoring on a Schedule is a free MLOps Academy lesson on CoddyKit — lesson 1 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.
What Batch Scoring Is
Batch scoring runs your model over a whole dataset at once, on a schedule, and saves every prediction for later use. 📦
No User Is Waiting
In batch mode nobody waits for a live answer. The job runs offline, so a few minutes or hours of runtime is perfectly fine.
A Classic Example
Think of scoring churn risk for every customer each night. One job reads the table, predicts for all rows, and writes the results back.
Read, Predict, Write
Every batch job follows the same shape: load the input data, call predict on it, then store the output where consumers can read it.
df = load_data()
preds = model.predict(df)
save(df.assign(score=preds))Predict on Many Rows
Models love arrays. Passing a whole DataFrame to predict is far faster than looping one row at a time, because work is vectorized.
preds = model.predict(df[features])Where Results Live
Batch predictions land in a table, file, or warehouse. Apps then just look up the stored score instead of calling the model live.
Run It on a Schedule
A scheduler like cron or Airflow fires the job at a fixed time. This cadence can be hourly, nightly, or weekly to fit how fast data changes.
# crontab: run every day at 2am
0 2 * * * python score_batch.pyWhy Batch Is Cheap
You spin up compute, score everything, then shut it down. This burst pattern means you pay only for the minutes the job actually runs.
Freshness Is the Trade
The cost of batch is staleness. A score from last night may be hours old, so batch fits cases where slightly stale answers are acceptable.
Idempotent and Safe
Design the job so re-running it gives the same result. An idempotent job can be retried after a failure without creating duplicates.
When to Pick Batch
Choose batch when predictions cover known entities, freshness in hours is fine, and you want simplicity and low cost over instant answers.
Quick Check
What is the main downside of batch scoring?
Recap
Batch scoring predicts over a full dataset on a schedule, stores results for fast lookup, and trades freshness for low cost and simplicity.
Frequently asked questions
Is the “Batch Scoring on a Schedule” lesson free?
Yes — the full text of “Batch Scoring on a Schedule” 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 “Batch Scoring on a Schedule”?
Predict over a whole dataset and store results. 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 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Batch Scoring on a Schedule” 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