Chalk for MLOps
Build fully observable ML pipelines with automatic data lineage on every query.
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Trusted by teams building the next generation of AI + ML
Why MLOps engineers choose Chalk
Built-in versioning and audibility
Automatic versioning and audit trails for consistent, reproducible feature definitions
Feature monitoring and alerting
Monitoring with alerts so features remain fresh, accurate, and drift-free
Infrastructure observability
Logs for debugging, Kubernetes cluster activity, and system-level metrics with alerts
On-demand pre-computation
On-demand pre-computation that replaces brittle pipeline logic and manual orchestration
Tracing for query performance diagnosis
Tracing for query performance diagnosis
Tracing gives teams deep visibility into how queries run inside Chalk. Each resolver and model call is instrumented and timed, making it easy to identify performance bottlenecks and understand why a query behaves the way it does.
Tracing docs
Chalk makes it easy to get started, and its isolation model keeps everything safe by default. Teams don’t block each other anymore.
Melvin Lew MLOps Engineer
Reproducible and auditable features by default
Reproducible and auditable features by default
Chalk applies modern software engineering to ML workflows:
- View lineage from data sources through transformations to final outputs, with version history captured automatically
- Browse and search historical versions of every feature, query, resolver, and deployment in one unified feature catalog
- Audit changes and reproduce results instantly with complete traceability built into the dashboard
Built for scale and trust
Chalk runs in your cloud (AWS, GCP, Azure), meeting enterprise standards for security, compliance, and deployment flexibility.
DEPLOY IN YOUR CLOUDExplore how Chalk works
Ready to ship next-gen ML?
Talk to an engineer and see how Chalk can power your production AI and ML systems.
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