Batch Inference
Intermediate 13 min read Batch Inference

Batch Evaluation and Reconciliation

Judging the scores you just wrote

Batch jobs need evaluation too: quality metrics calculated over the scored window, plus reconciliation against ground truth when it arrives. Offline eval isn't online-monitoring's poor cousin — it's where regression gates actually get their evidence.

batch evaluationreconciliationground truthregression gatewindow quality
Prerequisites: Batch Inference Overview Model Performance Monitoring
Before we start

What you will learn

  • Define window-level quality metrics for batch runs
  • Reconcile predictions against arriving ground truth
  • Use batch evaluation as promotion gate evidence
Introduction

What is this?

Batch evaluation computes quality metrics on a scored window — against known labels when available, or against distributional baselines when not — and reconciles predictions with ground truth as it arrives. It turns a batch output from data into evidence.

Motivation

Why do you need it?

  • Scores you write today can be wrong without anyone noticing until they're used.
  • Ground truth (labels, outcomes) arrives later; reconciliation catches drift over time.
  • Regression gates need per-window evidence to promote or pause a model.
  • 'Numbers look fine' is replaced by 'metrics held across windows'.
Think about it

Mental model

Imagine

An exam grader returning marks with a re-mark policy

Think of

Batch reconciliation

Grades (scores) come out on the schedule, but a student can appeal when the official answer key (ground truth) lands — discrepancies get caught and fixed, and the school calibrates if too many appeals succeed (drift).

Mechanics

How it works

  • Per window, compute metrics: row coverage, distribution stats, bias checks vs baseline.
  • A reconciliation job joins predictions with labels as labels arrive.
  • Difference metrics (MAE, AUC on labeled set) are computed and trended per window.
  • Threshold breaches raise alerts or trigger retraining through the pipeline.
  • The batch eval output feeds the promotion/rollback gates.

Batch evaluation against arriving labels.

Window scores
Labels arrive
Reconcile
Trend vs baseline
Alert or gate
Real world

In production

  • Churn predictions are reconciled weekly against churned/not label tables.
  • A batch drift job compares window distributions to detect feature drift without labels.
  • The regression gate for a model promotion reads the last 14 windows' reconciliation trend.
Guidelines

Best practices

  • Define window quality as a standard, versioned artifact of every run.
  • Reconcile on the join key, not by row index.
  • Trend over windows, don't react to single-window noise.
  • Report coverage — if labels only cover 40% of rows, say so and trend it.
Watch out

Common mistakes

  • Evaluating only on train-time metrics and ignoring per-window reality.
  • Reconciling by index when the join key is the only honest connection.
  • Reacting to one noisy window — no trend, no confidence.
  • Coverage never reported, so 'metric fine' hides missing labels.
When things break

Debugging tips

  • 'Metrics soared then vanished' — check label coverage; a thin label period mocks a real spike.
  • Reconciliation mismatch — verify join keys and label definition; semantic drift hits hard.
  • Gate paused a model — read the reconciliation trend, not the latest window number alone.
Interviews

Interview checkpoint

Test yourself

Quick quiz

0/2
1

The honest join seed batch evaluations use is…

2

A single window's reconciliation number is enough to roll back a model.

Go deeper

Official documentation

Evidently AI — drift and quality reports
docs