ML Model Governance Platform

20 models in production.
One audit trail.

Your experiment tracker records what happened. Your artifact registry stores the weights. Neither tells the compliance team who authorized the retrain, what policy governed it, or why this version replaced the last one. Inferpathio is the governance layer that does.

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40+ enterprise ML teams 200K+ model versions tracked SOC 2 controls built-in

Inferpathio governance dashboard showing model health matrix with drift alerts and approval queue

The Problem

Your models ship to prod. Then what?

W&B is an excellent experiment tracker. MLflow is a solid artifact registry. Neither was designed to answer: who approved this retrain? Under what policy? What is the immutable record of every decision that put this model version in production?

Your compliance team needs a reproducible audit trail. Your platform team wants YAML-defined retrain policies that don't live in someone's head. Your on-call engineer needs to roll back to any ancestor version in under 60 seconds — with lineage intact.

None of those tools cover that. Inferpathio does.

Capability	Inferpathio	W&B	MLflow
Retrain Approvals
Drift Policy YAML
Audit Trail
Role-Based Access
1-Click Rollback
Compliance Export

How It Works

From signal to fix in 10 minutes

Detect

SDK sends drift alert

When PSI exceeds your configured threshold, the SDK fires a governance event automatically.

import inferpathio as ifp

tracker = ifp.ModelTracker(
    model_id='credit-risk-v3',
    policy='production-strict'
)

# Triggers governance pipeline
tracker.log_prediction_batch(
    features=X_batch,
    labels=y_actual,
    threshold=0.08  # PSI
)

Govern

Enters approval queue

Retrain request routes to the right approvers per your RBAC policy — ML Lead and Compliance Owner notified within minutes.

model: credit-risk-v3
retrain_trigger:
  psi_threshold: 0.08
  accuracy_floor: 0.91
approval:
  required: true
  approvers:
    - role: ml-lead
    - role: compliance-owner
  timeout_hours: 24

Retrain

Pipeline triggers

On approval, a webhook fires your training pipeline automatically. Every action is logged with actor, timestamp, and policy version.

{
  "event": "retrain.approved",
  "model_id": "credit-risk-v3",
  "approved_by": "[email protected]",
  "policy_version": "[email protected]",
  "trigger": {
    "type": "drift",
    "psi": 0.12
  },
  "pipeline_run_id": "run_8f3a2c1d"
}

Deploy

New version, full lineage

Approved version is promoted to production. Full DAG from experiment to deployment is stored — rollback to any ancestor in under 60 seconds.

{
  "event": "model.promoted",
  "model_id": "credit-risk-v3",
  "version": "v3.8",
  "replaces": "v3.7",
  "lineage_url":
    "app.inferpathio.com/lineage/v3.8",
  "rollback_available": true
}

Enterprise Governance

Built for the org, not just the notebook

Audit & Compliance

Complete decision lineage: who saw what, when, what action taken

Exportable audit reports for SOC 2 review cycles

RBAC & Approval

Per-model role assignments: ML Engineer, ML Lead, Compliance Owner

Tiered approval chains with SLA enforcement

Drift & Retrain Policy

YAML-defined policies per model, stored in version control

Automated retrain triggers with configurable thresholds

Version Lineage

Full DAG: experiment → candidate → production

Rollback to any ancestor in under 60 seconds

Integrations

GitHub Actions, Prefect, Airflow, MLflow artifacts, SageMaker endpoints

Custom webhooks + REST API

Policy editor interface showing RBAC configuration and approval chain settings

Quick Integration

Add governance in 3 lines

Works with your existing MLflow setup. No migration required.

MLflow SageMaker HuggingFace Airflow GitHub Actions

View full SDK docs

# Install via pip
pip install inferpathio

import inferpathio as ifp
ifp.init(api_key=os.getenv('INFERPATH_KEY'))

# Attach governance to any model log
with ifp.governance_run(
    model_id='fraud-detector-v2'
) as run:
    run.log_model(model, flavor='sklearn')
    run.set_policy('production-strict')
    run.tag('env', 'production')

Customer Stories

What happens when you add a governance layer

"Before Inferpathio, when our compliance team asked why the credit-score model had been retrained in Q4, the answer required three Confluence docs, two retrospective threads, and a week of calendar archaeology. Now the audit export takes about 90 seconds. Our last SOC 2 review cycle took 2 hours instead of 2 days to produce the ML evidence package."

Jordan Mbeki

ML Platform Lead, Cortivance Analytics

"We manage 35 production models across six business units. The challenge isn't technical — it's organizational. With per-model RBAC policies, each team owns their own approval chain. The retrain queue replaced a weekly synchronization meeting. That meeting had been running for two years."

Priya Sundaram

Director of AI Engineering, Axiom Freight

Compliance-ready from day one

Designed for enterprise teams navigating model governance audits, internal risk reviews, and cross-team accountability.

Immutable Audit Trail

Every governance event logged with actor, timestamp, policy version, and outcome. No modification or deletion by any role — including admin.

SOC 2 Controls

Infrastructure built with SOC 2 Type II controls: AES-256 at rest, TLS 1.3 in transit, RBAC, and vulnerability management. Not yet certified — see our security page for what that means.

PII-Aware Feature Handling

PII fields in prediction feature vectors are tagged and isolated. Consent-layer hooks let you exclude regulated fields from drift computation without changing your model code.

Per-Model Role Isolation

Access to each model's governance events, approval chain, and drift data is scoped independently. Team A cannot approve or view Team B's retrain queue.

20 models in production.One audit trail.