Judges the work
Fabric Experiments
A Databricks adoption accelerator for quality engineering, experimentation, and governed delivery.
You can answer whether the new version is better, mechanically. Assignment, evaluation, and quality gates run against your own Databricks data, so shipping a model or agent change stops depending on somebody's judgement call.
3 common questions, answered below ↓- Role in the family
- Judges the work
- Databricks surfaces
- MLflowUnity CatalogDeltaLakeflowModel ServingAI Gateway
- Status
- Live across a typed API, a CLI, a studio UI, Cloudflare edge workers for assignment and ingestion, and Temporal for durable workflows.
- Documentation
- experiments.fabric.pro
The problem
Teams ship a model or an agent and cannot say whether the new version is better than the old one. Evaluation lives in notebooks, experiment assignment is ad hoc, and nothing blocks a regression from reaching production.
How it works
Built on Databricks SQL, Delta, Lakeflow, Unity Catalog, managed MLflow, Model Serving, AI Gateway, Apps, and Lakebase. Databricks stays authoritative for compute, data, lineage, and governance; Experiments adds A/B assignment, cross-workload evidence, and production quality gates. Native MLflow runs link into the quality centre while their traces and artifacts stay in Databricks.
What it does
Quality gates that block
A gate is a policy checkpoint. A failed evaluation stops a promotion rather than annotating it.
Signed manifests at the edge
Experiment assignment is served from a signed, key-verified manifest at the edge.
Durable exposure ingestion
Exposure and conversion events queue, retry, and land as idempotent batches for Auto Loader.
Linked MLflow evidence
Native runs are referenced, not copied. Datasets, traces, and lineage stay in Databricks.
You can answer whether the new version is better, mechanically. Assignment, evaluation, and quality gates run against your own Databricks data, so shipping a model or agent change stops depending on somebody's judgement call.
FAQ
Fabric Experiments, answered
Does this replace MLflow?
No. Databricks stays authoritative for compute, data, lineage and governance. Native MLflow runs are referenced rather than copied, so datasets, traces and artifacts stay where they are, and Experiments adds assignment, gates and cross-workload evidence on top.
How do we know a new version is actually better?
Assignment, evaluation and quality gates run against your own Databricks data, so the comparison is mechanical rather than a judgement call. A failed evaluation blocks the promotion.
Can we run experiments on agents, not just models?
Yes. The same gates apply to an agent version as to a model version, which is what lets you ship agent changes at a pace without guessing at the effect.