Point-in-Time Evaluation, Approval, and Batch Scoring

Governed Model Lifecycle for Sales Analytics

Extended a Virtual Data Scientist workbench with a local deal-risk lifecycle: deterministic weekly opportunity snapshots, a versioned feature contract, chronological evaluation, fixed registration gates, explicit human approval, and approval-gated batch scoring with reason codes and model lineage.

★0.716 Average Precision
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Challenge

A model catalog is not useful if users can apply opaque artifacts to arbitrary warehouse tables. Reusable analytical models need a fixed prediction question, point-in-time feature meanings, leakage controls, comparable benchmarks, approval state, and traceable batch outputs.

Solution

Extended a Virtual Data Scientist workbench with a local deal-risk lifecycle: deterministic weekly opportunity snapshots, a versioned feature contract, chronological evaluation, fixed registration gates, explicit human approval, and approval-gated batch scoring with reason codes and model lineage.

Impact Metrics

0.716
Average Precision
from 0.312
0.137
Brier Score
from 0.201
28%
Top-Decile Recall
from 12%
15
Workflow Tests
passing

Results

  • • Evaluated 360 deterministic weekly snapshots with 270 training and 90 later held-out records
  • • Improved average precision from 0.312 to 0.716 and Brier score from 0.201 to 0.137
  • • Raised recall within the top 10% review capacity from 12% to 28%
  • • Rejected forbidden post-outcome fields before training or scoring
  • • Caught and fixed a label-based tie-breaking defect in the average-precision implementation
  • • Verified evaluate, review, approve, and batch-score behavior through 15 automated tests and browser checks

Business Impact

Shows how a Virtual Data Scientist can move beyond one-off analysis into governed model reuse while preserving point-in-time validity, human review, reproducibility, and an AWS-native path to managed training and batch inference.

Architecture

Governed Analytical Model LifecycleContractpoint-in-timeAuditleakage gateEvaluatetime holdoutRegistermodel cardApprovehuman gateScorebatch + lineageBenchmarkAP 0.312 → 0.716Registry StatePending → ApprovedOutputsrisk • reasons • lineageLocal POC today • SageMaker Pipelines, Registry, and Batch Transform target path

The benchmark uses deterministic synthetic sales data and validates the lifecycle, not expected production deal-risk accuracy. SageMaker Pipelines, Model Registry, Batch Transform, S3, Step Functions, and CloudWatch are documented target services rather than resources created for this local slice.

Virtual Data Scientist

See how the governed model lifecycle fits inside the broader agentic analytics workbench without moving runtime code into the portfolio site.

Open Project Overview

Evaluate Before You Operationalize

A proven approach combining statistical rigor, automation, and AWS best practices.

1

Fix the Use Case

Define the weekly observation time, 30-day outcome horizon, intended user, prohibited use, and feature contract before selecting a model.

2

Protect the Holdout

Split records chronologically and fail the audit when a feature contains information created after the prediction timestamp.

3

Beat a Transparent Baseline

Compare constant-risk and logistic-regression candidates using calibration, average precision, and recall at human review capacity.

4

Register, Approve, Score

Store evidence as a version, require explicit human approval, then emit batch predictions with reason codes and lineage.

Scale & Scope

360
Dataset
synthetic snapshots - point-in-time weekly records
90
Holdout
held-out records - strictly later evaluation window
4
Lifecycle
lifecycle stages - evaluate, review, approve, batch score

Technology Stack

  • Python standard library
  • Logistic regression from first principles
  • Chronological evaluation
  • Versioned model cards
  • Human approval gates
  • Batch reason codes and lineage
  • AWS AgentCore runtime
  • SageMaker target architecture

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