Data Lab on every pipeline
Interactive analysis fed by the same AI agents: profile, detect anomalies, visualize, then send the session to training. The lab is not a sidecar — it is the output of the pipeline.
Agents on the same output
Profiling
Column stats, types, and quality scores from sample rows before any LLM call.
Anomaly & quality
Drift, outliers, and scorecards the same crew that built the pipeline can explain.
Visualization
Charts and artifacts generated against the live profile, not a disconnected BI extract.
Ask in English
NL queries run as guided analyses on the session — then one click to ML Lab.
Why it stays first-class
Same pipeline output feeds Data Lab — no warehouse dump or notebook setup
Deterministic profiling first: nulls, distributions, quality scorecards
Agents for anomalies, correlations, and natural-language questions
Visualizations and artifacts stay attached to the session
Promote findings back into the pipeline or send the session to ML Lab
Reuse the latest session per pipeline so analysis stays cheap and current
Questions about this use case
No. Data Lab opens on the pipeline’s sample rows and profile. The latest session per user and pipeline is reused so you don’t bootstrap a new environment every time.
Analysis that can become a model
When the profile is good enough, send the session to ML Lab. Target scan, baseline, deploy — still on the same pipeline.