DATA ENGINEERING · SC-11
ML / AI Evaluation & Monitoring Platform
A production-oriented layer for model evaluation, version tracking, drift checks and release gates.
Starting point
The issue is reliability: inconsistent contracts, late failures and unclear ownership turn analytics into manual reconciliation.
01 · BUSINESS PROBLEM
Data becomes expensive when every downstream use rebuilds the same logic.
The issue is reliability: inconsistent contracts, late failures and unclear ownership turn analytics into manual reconciliation.
Broken inputs
Schema or source changes reach consumers too late.
Repeated logic
Teams rebuild transformations and definitions independently.
Slow diagnosis
Failures are difficult to locate across the data path.
02 · DECISION LOGIC
From raw event to trusted analytical state.
Quality is checked before the data becomes someone else’s decision problem.
Decision sequence
Each stage answers a different operating question
Capture data with explicit contracts.
Create stable analytical grain.
Fail early on quality and schema rules.
Expose governed data to downstream users.
Decision rule — Trust is built upstream, before a dashboard or model consumes the data.
03 · WHAT CHANGED
One governed path from source to consumption.
Extraction, transformation, quality and serving stay separated and observable.
Define source and schema contracts.
Separate raw, staging and business-ready layers.
Automate tests and release gates.
Monitor latency, freshness and failures.
04 · ARCHITECTURE
A modular path from input to decision.
Inputs → preparation → core logic → validation → decision output
SC-11 · SYSTEM ARCHITECTURE
Inputs → preparation → core logic → validation → decision output
Public portfolio implementation
Inputs
Source signals
Capture the operating inputs required by the system. [Python]
Preparation layer
Normalize context and create a stable analytical contract. [MLflow]
Core system
Core engine
Run the main analytical or automation logic. [Evidently]
Decision logic
Apply the rule, model or orchestration logic that changes the decision. [FastAPI]
Validation
Validation
Test outputs against explicit quality criteria. [PostgreSQL]
Controls
Keep approvals, thresholds or constraints visible. [Prometheus]
Decision output
Decision output
Expose the result in a form the user can act on. [Kubernetes]
Monitoring
Record outcomes, exceptions and evidence for iteration. [Docker]
Integration boundaries
Python
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
MLflow
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
Evidently
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
05 · EVIDENCE & ECONOMICS
Measure what changes the decision.
Public implementation, inspectable technical proof and decision-focused validation.
Platform quality
Sources
7
Representative public example.
Quality tests
29
Representative public example.
Valid rows
99.6%
Representative public example.
Operational coverage
Target latency
<15m
Representative public example.
Data layers
3
Representative public example.
Reference economics
40 h
Reference scenario
50%
Illustrative improvement
20 h
Decision value
06 · TECHNICAL PROOF
Review the code behind the project.
Tools used
BUSINESS CONCLUSION
Good data engineering makes downstream decisions boringly reliable.
The value is less reconciliation, fewer silent failures and a faster path from operational events to trusted decisions.