How do I install the Ml Engineering skill?

Run npx skillmds@latest add irahardianto/ml-engineering in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ml Engineering safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ml Engineering?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ml Engineering free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ml Engineering?

irahardianto (@irahardianto) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ml Engineering by irahardianto — Agent Skill

Name: Ml Engineering
Author: irahardianto

File contents

ML Engineering Principles

Guidelines for building reliable, reproducible machine learning systems.

When to Invoke

Designing ML pipelines (training, serving)
Feature engineering and data preparation
Model evaluation and validation
MLOps infrastructure decisions

ML Pipeline Design

Stages

Data Collection → Feature Engineering → Training → Evaluation → Deployment → Monitoring

Principles

Reproducibility — versioned data, code, and config. Same inputs = same model.
Experiment tracking — every run logged (MLflow, W&B, Neptune).
Feature stores — centralized feature computation, reusable across models.
Model registry — versioned models with metadata, promotion workflow.

Feature Engineering

Compute features once, reuse everywhere — feature store pattern.
Training-serving skew prevention — same transformation code in training and inference.
Feature documentation — every feature has description, source, freshness requirement.

Model Validation

Checklist

Performance metrics meet threshold (accuracy, F1, AUC, etc.)
No data leakage (target info in features)
Fairness evaluation across protected groups
Performance on edge cases and out-of-distribution data
Latency meets serving SLA
Model size within deployment constraints

Model Serving

Pattern	When
Batch inference	Scheduled predictions, large volumes, latency-tolerant
Real-time API	Low-latency, per-request predictions
Streaming	Continuous predictions on event streams
Edge	On-device, offline-capable

Monitoring

Data drift detection — statistical tests on input distributions.
Model performance monitoring — track prediction accuracy over time.
Feature importance drift — alert when feature contributions shift.
Automated retraining triggers — retrain when performance degrades below threshold.

Tools Ecosystem

Category	Tools
Experiment tracking	MLflow, Weights & Biases, Neptune
Feature stores	Feast, Tecton, Hopsworks
Model registry	MLflow, Vertex AI, SageMaker
Data versioning	DVC, LakeFS
Pipeline orchestration	Kubeflow, Vertex AI Pipelines, Airflow

Data Engineering @.gemini/skills/data-engineering/SKILL.md
Python Idioms @.gemini/skills/python-idioms/SKILL.md
Performance Optimization Principles @.gemini/skills/performance-optimization-principles/SKILL.md

---name: ml-engineeringdescription: ML pipeline design, feature engineering, model training/serving, experiment tracking, model validation, and MLOps principles.---# ML Engineering PrinciplesGuidelines for building reliable, reproducible machine learning systems.## When to Invoke- Designing ML pipelines (training, serving)- Feature engineering and data preparation- Model evaluation and validation- MLOps infrastructure decisions## ML Pipeline Design### Stages```Data Collection → Feature Engineering → Training → Evaluation → Deployment → Monitoring```### Principles1. **Reproducibility** — versioned data, code, and config. Same inputs = same model.2. **Experiment tracking** — every run logged (MLflow, W&B, Neptune).3. **Feature stores** — centralized feature computation, reusable across models.4. **Model registry** — versioned models with metadata, promotion workflow.## Feature Engineering1. **Compute features once, reuse everywhere** — feature store pattern.2. **Training-serving skew prevention** — same transformation code in training and inference.3. **Feature documentation** — every feature has description, source, freshness requirement.## Model Validation### Checklist- [ ] Performance metrics meet threshold (accuracy, F1, AUC, etc.)- [ ] No data leakage (target info in features)- [ ] Fairness evaluation across protected groups- [ ] Performance on edge cases and out-of-distribution data- [ ] Latency meets serving SLA- [ ] Model size within deployment constraints## Model Serving| Pattern | When ||---|---|| **Batch inference** | Scheduled predictions, large volumes, latency-tolerant || **Real-time API** | Low-latency, per-request predictions || **Streaming** | Continuous predictions on event streams || **Edge** | On-device, offline-capable |## Monitoring1. **Data drift detection** — statistical tests on input distributions.2. **Model performance monitoring** — track prediction accuracy over time.3. **Feature importance drift** — alert when feature contributions shift.4. **Automated retraining triggers** — retrain when performance degrades below threshold.## Tools Ecosystem| Category | Tools ||---|---|| Experiment tracking | MLflow, Weights & Biases, Neptune || Feature stores | Feast, Tecton, Hopsworks || Model registry | MLflow, Vertex AI, SageMaker || Data versioning | DVC, LakeFS || Pipeline orchestration | Kubeflow, Vertex AI Pipelines, Airflow |## Related- Data Engineering @.gemini/skills/data-engineering/SKILL.md- Python Idioms @.gemini/skills/python-idioms/SKILL.md- Performance Optimization Principles @.gemini/skills/performance-optimization-principles/SKILL.md

Frequently asked questions

Ml Engineering