0Pricing
MLOps Academy · Lesson

Trace a Prediction Round-Trip

Follow one request from input to logged response.

Trace a Prediction Round-Trip 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.

Follow One Request

Time to watch the whole loop work. You will follow a single prediction from a client request all the way to a logged response. 🔍

The Client Sends Input

A caller POSTs JSON to /predict. This is the raw input feature payload your model needs to make a decision.

curl -X POST localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"tenure": 12, "monthly_charges": 79.9}'

Pydantic Validates

FastAPI parses the body against your schema. If a field is missing or the wrong type, Pydantic rejects it with a 422 before the model runs.

The Model Predicts

The validated row hits the loaded champion model. It computes a result in memory, no retraining and no registry call on the hot path.

pred = app.state.model.predict([[f.tenure, f.monthly_charges]])

Log What Happened

Before responding, write a structured log line with the inputs, the prediction, and a timestamp. This is your audit trail.

import logging, json
logging.info(json.dumps({
    "input": f.model_dump(),
    "prediction": int(pred[0]),
}))

Return the Response

FastAPI serializes your dict back to JSON and sends it with a 200. The client receives the prediction in milliseconds.

{"prediction": 1}

Capture Latency

Wrap the call to measure how long it took. Logging latency per request is what later powers your monitoring dashboards.

import time
start = time.perf_counter()
# predict...
elapsed_ms = (time.perf_counter() - start) * 1000

Tie Back to the Version

Log which model produced the answer. Recording the version means you can always say exactly which model made any past prediction.

{"model": "churn-classifier", "version": 3}

The Loop Closed

Train, register, promote, serve, predict, log: one request just walked the entire pipeline you built. That is end-to-end MLOps. 🎯

Quick Check

Why log the input, prediction, and model version on every request?

Logs Feed Monitoring

Those logged round-trips become the raw material for the next stage: monitoring latency, error rates, and eventually input drift over time.

You Built a System

This is no longer a notebook. It is a traceable system where every prediction can be explained and reproduced from end to end.

Recap: The Full Round-Trip

You traced one request through validation, prediction, logging, and response, tying it back to a model version. That closes your first end-to-end flow. ✅

Frequently asked questions

Is the “Trace a Prediction Round-Trip” lesson free?

Yes — the full text of “Trace a Prediction Round-Trip” 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 “Trace a Prediction Round-Trip”?

Follow one request from input to logged response. 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 “Trace a Prediction Round-Trip” 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

  1. Train and Log to the Registry
  2. Promote the Best Model to Production
  3. Serve the Production Model
  4. Trace a Prediction Round-Trip
← Back to MLOps Academy