Monitoring & Alerting

Data drifts. Pipelines break.
Find issues before your customers do.

It’s inevitable — production data drifts from historical baselines, pipelines break, and partners change data formats. Chalk automatically monitors the execution of your feature pipelines and the distributions of features to alert you when problems arise.

CPU Utilization

The feature distribution of your target population might change over time. Easily differentiate between organic shifts, unexpected format changes in upstream data sources, and development mistakes. Get alerted automatically before issues cause your model performance to suffer.

Pipeline execution

When feature engineering pipelines break, you need visibility into why. Aggregate logs and metrics by query, cron job, migration, and even individual resolvers. Every line of code you write is automatically instrumented so you can easily diagnose issues.

When ETL pipelines break or third-party data vendors have outages, models can wind up with stale inputs that lead to inaccurate predictions. Monitor freshness across batch, streaming, and realtime data sources to make sure that your models execute with up-to-date data.

Data lineage

Upstream data quality issues can cause your features to suffer. Automatically track data provenance for derived features, so that you can understand which upstream data sources cause problems and escalate issues to the relevant data owners or external vendors.

Production-grade machine learning requires production-grade alerting. Integrate with the alerting systems you already use like Pagerduty, or chat systems like Slack, to keep your team informed about issues. Configure alerting thresholds so that you get notified when pipeline behavior doesn’t match expectations.

Get Started with Code Examples
Unlock the power of real-time data pipelines
Feature Discovery
Tags & Owners
Assigning tags & owners to features.
GitHub Actions
Preview deployments
Set up preview deployments for all PRs.
Serve many end-customers with differentiated behavior.
Unit tests
Resolvers are just Python functions, so they are easy to unit test.