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pilot-ml-training-pipeline-setup

Deploy a four-agent ML training pipeline coordinating data preparation, training, evaluation, and serving.

7|3|Updated Apr 8, 2026
Search Tags:#governance#distributed#pipeline#coordination#ml#model-training#machine-learning
One-click install
npx skills add https://github.com/TeoSlayer/pilot-skills --skill pilot-ml-training-pipeline-setup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pilot-ml-training-pipeline-setup
Source: https://github.com/TeoSlayer/pilot-skills/tree/main/skills/pilot-ml-training-pipeline-setup
Command: npx skills add https://github.com/TeoSlayer/pilot-skills --skill pilot-ml-training-pipeline-setup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deploy an end-to-end ML training pipeline that coordinates four agents: data-prep, trainer, evaluator, and serving, enabling seamless data flow, model sharing, and governance.

Core Features & Use Cases

  • End-to-end orchestration across the four roles, ensuring data moves from preparation to serving with traceability.
  • Role-specific workflows using pilot-dataset, pilot-share, pilot-model-share, pilot-metrics, and pilot-task-chain to coordinate tasks and artifacts.
  • Deployment scenarios include experimentation, benchmarking, and production-ready pipelines across distributed agents.

Quick Start

Install the ml-training-pipeline across the configured agents and begin handshake setup to start coordinating the four-role workflow.

Frequently Asked Questions about pilot-ml-training-pipeline-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate an end-to-end ML training pipeline across distributed agents?▼

You orchestrate an end-to-end ML training pipeline by deploying a four-agent coordination workflow spanning data preparation, training, evaluation, and serving. This enables seamless data flow, model sharing, and metrics tracking across distributed agents.

What is the best way to coordinate model sharing and metrics tracking in distributed machine learning workflows?▼

The best way to coordinate model sharing and metrics tracking is using a pipeline protocol that connects dedicated trainer and evaluator agents, ensuring artifacts and governance data move seamlessly between roles.

Can I use this ML pipeline setup for both experimentation and production benchmarking?▼

Yes, you can use this ML pipeline setup for experimentation, benchmarking, and production-ready workflows. It orchestrates role-specific tasks across distributed agents to support various deployment scenarios.

Do I need to configure agent handshakes before starting the machine learning workflow?▼

Yes, you need to install the pipeline across configured agents and complete a handshake setup. This establishes the coordination required to start the four-role machine learning workflow.

How does data flow between preparation and serving stages in an automated ML pipeline?▼

Data flows between preparation and serving stages through role-specific workflows that coordinate datasets, task chains, and model artifacts. This ensures end-to-end traceability and governance across the pipeline.

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