Log Predictions for Later Analysis
Capture features and outputs to a prediction store.
Log Predictions for Later Analysis is a free MLOps Academy lesson on CoddyKit — lesson 2 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.
You Cannot Debug What You Did Not Save
If you never store what your model saw and said, you cannot diagnose it later. Prediction logging is the foundation of all analysis. 📝
What a Prediction Log Holds
A useful prediction log captures the input features, the model output, the model version, and a timestamp for every request.
Add a Request ID
Give each prediction a unique request ID. Later, when the true outcome arrives, you join it back to the original input by that ID.
Log Inputs and Outputs Together
Always store the features alongside the prediction. A score with no inputs cannot tell you why the model decided what it did.
record = {
"request_id": rid,
"features": features,
"prediction": pred,
"model_version": "v3",
}Stamp the Model Version
Tag every log with the model version that produced it. After a rollout you can compare old and new behavior side by side.
Use Structured JSON
Write logs as structured JSON, not free text. Machines can then query, filter, and aggregate your predictions automatically.
The Prediction Store
Predictions usually flow into a prediction store: a database, warehouse, or data lake built for cheap, queryable history.
Log Asynchronously
Write logs asynchronously so storing a record never slows down the response your user is waiting for.
Mind Privacy
Logs may hold personal data, so apply privacy rules: hash identifiers, drop sensitive fields, and set a retention window.
Join Predictions to Outcomes
When real labels land, join them to the logs by request ID. That join is how you measure live accuracy after the fact.
Sample if Volume Is Huge
At massive scale, log a representative sample instead of every request to keep storage and cost under control.
Quick Check
Think about reconnecting a prediction to reality.
Recap
Log every prediction with its inputs, output, version, and request ID. That structured history is what every later analysis relies on. ✅
Frequently asked questions
Is the “Log Predictions for Later Analysis” lesson free?
Yes — the full text of “Log Predictions for Later Analysis” 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 “Log Predictions for Later Analysis”?
Capture features and outputs to a prediction store. 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 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Log Predictions for Later Analysis” 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
- The Four Pillars of ML Observability
- Log Predictions for Later Analysis
- Slice Metrics by Segment and Cohort
- Explain Predictions with SHAP