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diegouis
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ml-ai-assistant

Develop and deploy ML and AI systems with model training and MLOps pipelines.

Updated Aug 23, 2026
Search Tags:#deployment#aws#mlops#ai#llm#rag#model training
One-click install
npx skills add https://github.com/diegouis/provectus-marketplace --skill ml-ai-assistant
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-ai-assistant
Source: https://github.com/diegouis/provectus-marketplace/tree/main/plugins/proagent-ml-ai/skills/ml-ai-assistant
Command: npx skills add https://github.com/diegouis/provectus-marketplace --skill ml-ai-assistant

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the entire lifecycle of building and deploying Machine Learning and AI systems, from initial model training to advanced LLM application development.

Core Features & Use Cases

  • Model Training & Evaluation: Supports training various models, experiment tracking, and comprehensive evaluation.
  • LLM & RAG Development: Enables building sophisticated LLM applications, including Retrieval-Augmented Generation systems.
  • Deployment & Monitoring: Provides patterns for deploying models and monitoring their performance in production.
  • Use Case: You need to train a new classification model, track its performance against previous experiments, and then deploy it as a REST API.

Quick Start

Use the ml-ai-assistant skill to help train a new classification model.

Frequently Asked Questions about ml-ai-assistant

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

FAQPage Schema
How do I build a RAG system with LLMs and vector stores?▼

To build a RAG system, you use embeddings to index documents in vector stores and retrieve context for LLMs. This Skill facilitates Retrieval-Augmented Generation by integrating prompt engineering and knowledge graph data to ground model responses.

What's the best way to deploy ML models as a REST API on AWS?▼

Deploying ML models as a REST API on AWS requires MLOps pipelines for packaging and serving. This Skill provides deployment patterns and inference optimization strategies to serve trained models efficiently in production environments.

How do I set up experiment tracking for model training?▼

Experiment tracking for model training involves logging parameters and metrics to evaluate performance across runs. This Skill supports tracking experiments to compare new classification models against previous iterations systematically.

Can I use meta-prompting frameworks and LLM judge for evaluation?▼

Yes, you can use meta-prompting frameworks to guide generation and an LLM judge for evaluation. This Skill addresses advanced LLM application development by incorporating automated evaluation techniques to assess model output quality.

Does this approach work for integrating knowledge graphs with AI applications?▼

Integrating knowledge graphs with AI applications allows structured data to enrich LLM context. This Skill supports knowledge graph integration alongside vector stores to enhance retrieval capabilities within RAG systems.

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