SignalPilot for ML Engineers

Debug ML Pipelines Faster

SignalPilot connects to your feature stores, experiment trackers, and model registries to investigate performance regressions, data drift, and training failures.

Real Problems, Real Solutions

Model performance degraded in production

The Problem

Manually checking feature distributions, training/serving skew, and data drift across hundreds of features

With SignalPilot

Ask SignalPilot: 'Why did model accuracy drop 5% last week?' It analyzes feature drift, checks training data, and identifies the problematic features.

Training run failed after 6 hours

The Problem

Digging through logs, checking data pipelines, and validating input schemas to find the failure point

With SignalPilot

SignalPilot traces the failure, checks upstream data quality, and identifies whether it was a data issue, infra issue, or code bug.

Feature importance changed unexpectedly

The Problem

Comparing model versions, checking feature engineering code, and validating data transformations

With SignalPilot

Natural language: 'Compare feature importance between model v2.3 and v2.4.' SignalPilot pulls experiment metadata and generates the analysis.

Built for How You Work

SignalPilot integrates deeply with your existing tools and workflows.

Context-Aware ML Debugging

Connect your MLflow, Weights & Biases, or SageMaker. SignalPilot understands your experiment structure, model versions, and can compare runs automatically.

Privacy Mode for Sensitive Data

Working with PII or sensitive training data? Privacy mode ensures all analysis happens locally. Your data never leaves your machine.

Agentic Troubleshooting

SignalPilot formulates hypotheses about why your model is failing, tests each one systematically, and presents findings with evidence from your actual data.

Governance Hooks

Enforce that all production queries include data quality checks. Block queries on sensitive tables outside business hours. Full audit trail of all data access.

Common Use Cases

Model performance debugging

Feature drift detection

Training data validation

Experiment comparison

Production monitoring

A/B test analysis

Works With Your Stack

SignalPilot integrates with the tools you already use.

MLflow
W&B
Feast
TensorFlow
PyTorch
SageMaker
I used to spend hours debugging why a model degraded. SignalPilot identified feature drift in a specific data source within minutes.
Senior ML Engineer, AI Startup

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