Model lifecycle & governance
Evidently
Apache-2.0 Python library plus self-hostable platform for evaluating and monitoring data and AI systems, including tabular data quality and drift tests with statistical methods, declarative test suites usable in CI/CD, and dashboards tracking metrics and test results over time for deployed models.
open_source · generally available · Research snapshot 2026-09-06
Visit the official product source ↗Where it fits
Model lifecycle & governance · Observability & traceability · Evaluation & testing
Useful conversation with: ML engineer, Data scientist, MLOps lead.
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Show me a drift test suite failing in CI for a production model's input data, and how the result appears on the monitoring dashboard over time.
Capabilities and evidence
Support labels reflect the supplied research. Documentation and vendor claims are not independent product tests. “Not established” means the researcher did not find support; it does not prove a capability is absent.
Documented by provider
The Evidently repository states support for data drift detection with 20+ statistical tests and distance metrics, drift presets such as PSI, and test suites with pass/fail conditions usable for regression testing, CI/CD checks and data validation, plus a monitoring UI visualising metrics and test results over time.
Limit: Repository documents metrics and tests; it documents no model registry, approvals or lifecycle governance, and no vendor legal entity is named on the page.
Source s1
Documented by provider
Evidently documentation states the product is open source under Apache 2.0, available as a standalone Python library or as part of a self-hosted platform, covering evaluation, testing and monitoring of data and AI systems with 100+ metrics, a declarative testing API, tracing and dashboards.
Limit: Docs confirm licence and deployment forms; managed cloud features and limits were not reviewed.
Source s2
Limitations to discuss
- Categorised under model lifecycle governance for drift/performance monitoring; it does not provide registry, approval or regulatory features.
- Company details (legal entity, HQ) not stated on the pages fetched.
Sources
- evidentlyai/evidently — open-source ML and LLM observability framework · GitHub / Evidently AI · official repository
Access date reported by researcher: 2026-09-06 - Evidently documentation — introduction · Evidently AI · official docs
Access date reported by researcher: 2026-09-06
Listing does not imply partnership, supplier status, a working DutyGraph integration, or a compliance certification.
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