Add an episode on how AI support transforms interoperable infrastructures....

[leilaicruz]

• I think it can be a valuable episode to catch up with the state of the art practices on how AI will transform and can be useful when it comes to data interoperability

[leilaicruz]

It looks like : https://4turesearchdata-carpentries.github.io/interoperability-climate-sciences/AI.html

---

title: "Interoperable Infrastructure in the AI Era"
teaching: 20 # teaching time in minutes
exercises: 0 # exercise time in minutes

:::::::::::::::::::::::::::::::::::::: questions

• What are the requirements for an AI-ready data infrastructure in climate science?
• Why is interoperability crucial for AI applications in climate science?
• What are the key elements of an AI-ready interoperable data infrastructure?

::::::::::::::::::::::::::::::::::::::::::::::::

::::::::::::::::::::::::::::::::::::: objectives

• Understand the requirements for an AI-ready data infrastructure in climate science.
• Recognize the importance of interoperability for AI applications in climate science.
• Identify the key elements of an AI-ready interoperable data infrastructure.

::::::::::::::::::::::::::::::::::::::::::::::::

Content:

✔ AI needs:
• Large-scale multidimensional datasets
• Consistent CF metadata
• Chunked cloud-native formats
• STAC-like discoverability
• Stable APIs for pipeline automation
✔ Challenges
• Data fragmentation
• Lack of standardization
• FAIR gaps
• Poorly documented repositories
✔ Key elements of an AI-ready infrastructure
• CF metadata
• Community formats
• Cloud-native layouts
• Stable APIs
• STAC catalogs
• Versioning & identifiers
✔ Interoperability determines:
• Efficient access
• Reproducibility
• Integrability
• Trust in results
✔ Example
• FAIR-EO (FAIR Earth Observations)

:::::::::: keypoints

• AI-ready data infrastructure requires large-scale multidimensional datasets, consistent CF metadata, chunked cloud-native formats, STAC-like discoverability, and stable APIs for pipeline automation.
• Interoperability is crucial for AI applications in climate science as it enables efficient data access, reproducibility of results, integrability of diverse datasets, and trust in AI-driven insights.
• Key elements of an AI-ready interoperable data infrastructure include adherence to community formats, cloud-native layouts, stable APIs, comprehensive data catalogs, and robust versioning and identifier systems.

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