pipeline▌

ML pipeline automation orchestrates the entire machine learning workflow from data ingestion through model deployment, ensuring reproducibility, scalability, and reliability.

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies.

Blender Web Pipeline skill provides workflows for exporting 3D models and animations from Blender to web-optimized formats (primarily glTF 2.0). It covers Python scripting for batch processing, optimization techniques for web performance, and integration with web 3D libraries like Three.js and Babylon.js.

Data cleaning pipelines transform raw, messy data into clean, standardized formats suitable for analysis and modeling through systematic handling of missing values, outliers, and data quality issues.

Build automated continuous integration and deployment pipelines that test code, build artifacts, run security checks, and deploy to multiple environments with minimal manual intervention.

Build multi-step content creation pipelines via inference.sh CLI.

Build RAG (Retrieval Augmented Generation) pipelines via inference.sh CLI.

Orchestrator-only workflow for migrating or rewriting codebases. You do NOT read files, write code, or validate anything yourself. You only instruct agents and pipe context (paths, not contents).

Design and implement a complete ML pipeline for: $ARGUMENTS