moai-domain-ml
Machine learning model training, evaluation, deployment, and MLOps workflows.
When & Why to Use This Skill
The moai-domain-ml skill is a specialized toolkit designed for Machine Learning (ML) engineers and developers to streamline model training, evaluation, and MLOps workflows. It integrates industry-standard frameworks like PyTorch 2.5.0, TensorFlow 2.18.0, and MLflow 2.19.0, ensuring that AI development follows rigorous quality standards, including TRUST 5 principles and test-driven development (TDD) with high coverage requirements.
Use Cases
- End-to-End MLOps: Automate the entire machine learning lifecycle, including model training, performance evaluation, and production-ready deployment.
- Framework-Specific Development: Build and optimize complex neural networks using the latest stable versions of PyTorch and TensorFlow.
- Quality & Compliance Reviews: Conduct automated code reviews to ensure ML implementations adhere to TRUST 5 principles and maintain a minimum of 85% test coverage.
- Experiment Tracking: Manage model versions and track experiment metadata efficiently using integrated MLflow support for better reproducibility.
- Architecture Troubleshooting: Identify and resolve bottlenecks or dependency issues in ML pipelines using integrated terminal and file-reading capabilities.
| name | moai-domain-ml |
|---|---|
| version | 2.0.0 |
| created | 2025-10-22 |
| updated | 2025-10-22 |
| status | active |
| description | Machine learning model training, evaluation, deployment, and MLOps workflows. |
| keywords | ['ml', 'machine-learning', 'mlops', 'pytorch', 'tensorflow'] |
Domain Ml Skill
Skill Metadata
| Field | Value |
|---|---|
| Skill Name | moai-domain-ml |
| Version | 2.0.0 (2025-10-22) |
| Allowed tools | Read (read_file), Bash (terminal) |
| Auto-load | On demand when keywords detected |
| Tier | Domain |
What It Does
Machine learning model training, evaluation, deployment, and MLOps workflows.
Key capabilities:
- ✅ Best practices enforcement for domain domain
- ✅ TRUST 5 principles integration
- ✅ Latest tool versions (2025-10-22)
- ✅ TDD workflow support
When to Use
Automatic triggers:
- Related code discussions and file patterns
- SPEC implementation (
/alfred:2-run) - Code review requests
Manual invocation:
- Review code for TRUST 5 compliance
- Design new features
- Troubleshoot issues
Tool Version Matrix (2025-10-22)
| Tool | Version | Purpose | Status |
|---|---|---|---|
| PyTorch | 2.5.0 | Primary | ✅ Current |
| TensorFlow | 2.18.0 | Primary | ✅ Current |
| MLflow | 2.19.0 | Primary | ✅ Current |
Inputs
- Language-specific source directories
- Configuration files
- Test suites and sample data
Outputs
- Test/lint execution plan
- TRUST 5 review checkpoints
- Migration guidance
Failure Modes
- When required tools are not installed
- When dependencies are missing
- When test coverage falls below 85%
Dependencies
- Access to project files via Read/Bash tools
- Integration with
moai-foundation-langsfor language detection - Integration with
moai-foundation-trustfor quality gates
References (Latest Documentation)
Documentation links updated 2025-10-22
Changelog
- v2.0.0 (2025-10-22): Major update with latest tool versions, comprehensive best practices, TRUST 5 integration
- v1.0.0 (2025-03-29): Initial Skill release
Works Well With
moai-foundation-trust(quality gates)moai-alfred-code-reviewer(code review)moai-essentials-debug(debugging support)
Best Practices
✅ DO:
- Follow domain best practices
- Use latest stable tool versions
- Maintain test coverage ≥85%
- Document all public APIs
❌ DON'T:
- Skip quality gates
- Use deprecated tools
- Ignore security warnings
- Mix testing frameworks