Observe

What It Does

The observability layer creates a loop for ML lifecycle by continuously mapping production model performance directly back to foundational training data. When deployed models exhibit drift or edge-case failures, the system diagnoses root-cause degradation at the data tier rather than the architecture level. It automatically initiates an active learning feedback loop that routes edge cases through human-in-the-loop validation, commits a newly versioned dataset, and streams remediated training data directly into active retraining pipelines.

Model Registry

Where the AI-ready dataset and its trained model are registered together, the fixed reference point every prediction gets measured against.

Prediction Logs

Live predictions stream in from the platform actually running the model, captured as they happen in production.

Run Lifecycle

Each prediction is tested against ground truth provided manually, or derived through logical evaluation rules.

Drift Detection

Measures how far, and how unevenly, the model has drifted from its at-registration performance.

Run Drift Analysis

Traces drift back to the specific data responsible, the records actively holding the model below its best accuracy.

Refine data. Streamline retraining. Improve accuracy.

Re-labelling Loop

Flagged data re-enters labeling, review, and transformation in full, then lands back in the registry as a new version.

Native Connections

Connects directly to SageMaker, Vertex AI, Azure ML, and HuggingFace — predictions pull in automatically on a schedule, no manual export required.

Auto-Retrain Trigger

A flagged, relabeled dataset doesn't just wait for you — it can trigger retraining automatically on whichever platform the model already lives on.

Verified Outcome

The retrained model is scored on the exact same held-out rows the old one was — so you know the fix worked, instead of assuming it did because a new version exists.

Model Update Tracking

Retrained outside the platform entirely? Report the update directly — drift and accuracy re-baseline to traffic since that update, still verified against the held-out score from before. The loop doesn't require you to stay inside it to keep proving itself.

Capabilities that Define our Observability

Root-Cause Attribution Ensemble

Classifier AUC, real SHAP attribution, and embedding-space nearest-neighbor tracing run together to find exactly which feature shifted and which training rows caused it. Most platforms stop at "drift detected." This answers why.

Generative Drift Vocabulary

A distinct drift model for LLMs — topic-cluster PSI, new-topic emergence, refusal-rate drift. Classic label PSI is meaningless for generative output; this measures what actually degrades.

Multi-Modal Drift Engine

Real, distinct feature extraction per modality — image, audio, video — feeding one shared statistical core: PSI, KS-test, Wasserstein distance, real KDE curves. Not one generic check pretending to cover every modality.

Multi-Axis Fix Validation

The same held-out rows are checked for accuracy, calibration, and fairness. A win on one that regresses another is reported as a partial fix, never hidden behind one green number.

Compliance-Gated Autonomous Retrain

PII and consent enforcement extends into the automated retrain handoff, not just manual exports. An unattended pipeline that would ship unredacted PII gets silently blocked at the one step that matters — no human needed to catch it.

Guaranteed Stratified Hold-Out

Held-out selection is deterministic per label — every class with enough rows contributes at least one, with a 20-row floor — so a rare class can't silently vanish the way a naive random split would let it.

Refine data. Streamline retraining. Improve accuracy.

Capabilities that Define our Observability

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