domains / families / devops / ai-pipeline-orchestration

ai-pipeline-orchestration

combined

Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems. Orchestrate AI/ML pipelines for data ingestion, model training, batch

When to use

• Scheduling recurring RAG document ingestion and re-indexing
• Orchestrating multi-step batch LLM processing workflows
• Running nightly model evaluation and fine-tuning jobs
• Building ETL pipelines that feed into AI models
• Managing dependencies between data preparation and model serving

When not to use

• Guidance executes against real environments: confirm target, blast radius, and rollback plan before applying anything.
• Never deploy to production without explicit approval. Docs-only import: upstream scripts and templates not bundled.

Skill page

Conflict

The sickn33 copy is a mirrored, hardened version of the BagelHole skill. It preserves the same structure and examples but adds an 'Execution contract' section, changes the RAG example to a non-executable text block, and modifies the batch inference code to prevent duplicate billable calls (retries=0, explicit client close, stricter concurrency). Because the copies disagree on critical safety and cost parameters (retries, concurrency semantics, executability), crowning one would hide the safer alternative.