Online ML with
Drift Monitoring &
Autonomous Governance

What happens here

The system begins by ingesting raw data from a fixed source of truth. Before any modeling occurs, the data is treated as untrusted input.

A strict schema is applied:

• Required columns must exist
• Value ranges are enforced
• Categorical codes are validated
• Missing values are rejected

Why this matters

Most ML failures originate upstream. By enforcing validation early, downstream systems can rely on data correctness guarantees instead of defensive programming.

This phase optimizes for confidence, not convenience.

What happens here

Features are defined declaratively in a feature contract:

• Which columns are allowed
• Which are forbidden
• How each feature should be interpreted

Features are classified semantically:

This contract is shared across training, inference, monitoring, and retraining.

Why this matters

This is how the system prevents training–serving skew by construction.

If a feature is not in the contract, it does not exist.

Baseline & Candidates

Training is split into two explicit paths:

Models are trained only on frozen feature artifacts. No script touches raw data directly.

Multiple model families are evaluated: Logistic regression, Gradient-boosted trees.

Calibration is treated as mandatory, not optional.

Immutable Artifacts

Every trained model is registered as an immutable artifact bundle:

• ├── model.binary
• ├── preprocessor.pkl
• ├── metrics.json
• ├── calibration_data.csv
• └── metadata.yaml

Versions follow semantic versioning:

Why this matters

Rollback is trivial only if history is preserved. This registry turns models into deployable assets, not files.

Production Interface

The production model is exposed via a stateless HTTP API:

• Strict request schema
• Deterministic preprocessing
• Low-latency CPU inference

The model is loaded once at startup. Each request produces a probability, not a decision.

Why this matters

Separating prediction from decision-making preserves interpretability and reduces blast radius.

Observable Events

Every inference emits a structured log event:

Why this matters

Logs become data, not text. They power monitoring, debugging, and governance.

Temporal Windows

Logs are aggregated into time-windowed snapshots:

• Request volume
• Prediction statistics
• Feature distributions

Each snapshot is immutable and comparable.

Why this matters

Monitoring individual requests is noise. Snapshots capture behavior.

Statistical Measures

Current snapshots are compared to baselines using:

Volume sanity checks prevent false alarms.

Why this matters

Drift is measured, not guessed.

Policy-Based Automation

Drift metrics flow into explicit policies:

Decisions are rule-based and auditable.

Why this matters

Automation without policy is chaos. Policy without metrics is opinion.

Head-to-Head Comparison

When retraining is allowed, a candidate model is trained and compared head-to-head with production using identical data.

Promotion requires:

• No metric regressions
• Stable calibration
• No distribution instability

Risk-Free Validation

Candidate models run in parallel with production:

• Same inputs
• No user impact
• Paired logging

Differences are measured under real traffic.

Why this matters

Offline metrics lie. Production traffic does not.

Explicit States

The system tracks explicit states:

Transitions are logged and constrained.

Why this matters

Eligibility is not execution.

Governance Runner

A scheduled governance runner:

• Evaluates state
• Applies allowed transitions
• Executes promotion or rollback
• Exits cleanly

Manual overrides are respected.

Why this matters

The system is autonomous, not reckless.