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jluo41
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haipipe-end

Package trained ModelInstance_Set into Endpoint_Set and deploy to Databricks or local.

1|Updated Feb 16, 2026
Search Tags:#workflow#deployment#endpoint#builder#inference#mlflow#haipipe-end
One-click install
npx skills add https://github.com/jluo41/Tools --skill haipipe-end
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: haipipe-end
Source: https://github.com/jluo41/Tools/tree/main/plugins/research/skills/haipipe-end
Command: npx skills add https://github.com/jluo41/Tools --skill haipipe-end

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unified workflow to package a trained ModelInstance_Set into a production-ready Endpoint_Set, enabling seamless testing, design of five endpoint-inference function types (MetaFn, TrigFn, PostFn, Src2InputFn, Input2SrcFn), and deployment to Databricks or local environments.

Core Features & Use Cases

  • End-to-end lifecycle: packaging, testing, Fn design, and deployment across Databricks or local platforms.
  • Fn-type orchestration: supports all five Fn types with a guided builder workflow and YAML config.
  • Deployment orchestration: handles packaging, warmup, inference, and deployment steps in a repeatable process.

Quick Start

Run the haipipe-end workflow to package a trained endpoint, validate it with tests, and deploy to your chosen platform.

Frequently Asked Questions about haipipe-end

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

FAQPage Schema
How do I deploy a trained ML model as an inference endpoint?▼

You can design an ML inference endpoint by orchestrating five function types (MetaFn, TrigFn, PostFn, Src2InputFn, Input2SrcFn) alongside YAML configurations to structure the model's data processing and deployment logic.

Can I deploy my machine learning workflow to both Databricks and local environments?▼

Yes, the endpoint deployment workflow handles packaging, warmup, inference, and deployment steps to seamlessly deploy your machine learning models to either Databricks or local environments.

What inference functions do I need to configure for ML endpoint deployment?▼

You must configure five inference function types: MetaFn, TrigFn, PostFn, Src2InputFn, and Input2SrcFn. They are coordinated through a clear dispatch table and YAML configurations to structure endpoint behavior.

How do I package a trained model for production endpoint deployment?▼

You package a trained model for production endpoint deployment by converting the ModelInstance_Set into an Endpoint_Set, validating it through warmup and inference tests, and executing deployment steps via a repeatable orchestration process.

Does endpoint deployment require YAML configurations for inference functions?▼

Yes, YAML configurations are enforced during endpoint deployment to define and coordinate the behavior of the five inference function types, ensuring a repeatable packaging and testing workflow.

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