AGILAB: clearer onboarding for reproducible AI/ML workflows

[jpmorard]

We refreshed the public AGILAB repo around one clearer idea:

AGILAB is an open-source platform for reproducible AI/ML workflows, from local experimentation to distributed workers and long-lived services.

This launch wave tightens the first-time user path around a built-in flight_project demo and a more explicit README story.

What a first-time visitor should now see more quickly:

• what AGILAB is for
• how one app moves from local UI or CLI entrypoints to workers
• where service mode fits
• where analysis fits after execution

What changed in the public repo:

• clearer README positioning
• a more explicit quick-start path
• a 3-minute tour around flight_project
• clearer public positioning around reproducibility and orchestration
• better alignment between landing-page text, docs, and demo assets

If you work on applied ML systems and spend too much time stitching together setup, execution, remote runs, and analysis by hand, that is the workflow gap AGILAB is trying to reduce.

Repo:
https://github.com/ThalesGroup/agilab

Docs:
https://thalesgroup.github.io/agilab

Canonical demo assets:

• Flight demo video: https://github.com/ThalesGroup/agilab/blob/main/artifacts/demo_media/flight/agilab_flight.mp4
• Workflow explainer: https://github.com/ThalesGroup/agilab/blob/main/docs/source/diagrams/agilab_readme_tour.svg