ml-expert
ml-expert delivers comprehensive guidance on machine learning systems, from supervised and unsupervised learning through deep neural networks to production deployment. It covers model training workflows, hyperparameter optimization, and MLOps practices including versioning and monitoring. Includes practical code patterns for scikit-learn pipelines, PyTorch training loops, and FastAPI model serving.
ml-expert provides expert-level guidance for building, training, and deploying machine learning models with deep learning and MLOps practices.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-03-30
ml-expert provides expert-level guidance for building, training, and deploying machine learning models with deep learning and MLOps practices. ml-expert delivers comprehensive guidance on machine learning systems, from supervised and unsupervised learning through deep neural networks to production deployment. It covers model training workflows, hyperparameter optimization, and MLOps practices including versioning and monitoring. Includes practical code patterns for scikit-learn pipelines, PyTorch training loops, and FastAPI model serving.
Use it when
- ml-expert delivers expert guidance on production machine learning pipelines, including model versioning, experiment tracking.
- ml-expert offers detailed guidance on hyperparameter optimization techniques and model performance tuning.
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How to train deep learning models with ml-expert?
ml-expert provides comprehensive guidance on training deep learning models across PyTorch and TensorFlow frameworks. It covers supervised and unsupervised learning approaches, neural network architectures, and practical training workflows including data preprocessing, loss function selection, and optimization strategies. The skill includes code patterns for building training loops, handling GPU acceleration with CUDA, and implementing best practices to avoid common pitfalls during model development.
What MLOps best practices does ml-expert cover?
ml-expert delivers expert guidance on production machine learning pipelines, including model versioning, experiment tracking, and deployment strategies. It covers MLOps practices for monitoring models in production, setting up reproducible workflows, and implementing continuous integration for ML systems. The skill includes practical patterns for model serving with FastAPI and managing the full lifecycle from development through monitoring.
Can ml-expert help with hyperparameter tuning?
ml-expert offers detailed guidance on hyperparameter optimization techniques and model performance tuning. It covers systematic approaches to hyperparameter search, cross-validation techniques for robust evaluation, and strategies for optimizing model performance. The skill includes practical examples using scikit-learn and deep learning frameworks, helping you balance model complexity with generalization.
Does ml-expert support transfer learning with transformers?
ml-expert provides comprehensive coverage of transfer learning approaches, including transformer-based architectures and fine-tuning strategies. It guides you through leveraging pre-trained models for downstream tasks, adapting neural networks to new domains, and implementing efficient transfer learning workflows. The skill includes practical code patterns and best practices for working with modern deep learning architectures.
What feature engineering techniques does ml-expert teach?
ml-expert covers feature engineering for machine learning, including data preprocessing, scaling strategies, and feature selection methods. It provides guidance on transforming raw data into effective model inputs, handling missing values, and creating meaningful features that improve model performance. The skill includes practical patterns for scikit-learn pipelines and best practices for feature engineering workflows.
How does ml-expert help with model evaluation and metrics?
ml-expert delivers expert guidance on model evaluation metrics and assessment techniques across classification, regression, and deep learning tasks. It covers cross-validation approaches, metric selection for different problem types, and strategies for avoiding common evaluation pitfalls. The skill helps you understand model performance comprehensively and make informed decisions about model selection and deployment readiness.
SKILL.md
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Machine Learning Expert
Expert guidance for machine learning systems, deep learning, model training, deployment, and MLOps practices.
Core Concepts
Machine Learning Fundamentals
- Supervised learning (classification, regression)
- Unsupervised learning (clustering, dimensionality reduction)
- Reinforcement learning
- Feature engineering
- Model evaluation and validation
- Hyperparameter tuning
Deep Learning
- Neural networks (CNNs, RNNs, Transformers)
- Transfer learning
- Fine-tuning pre-trained models
- Attention mechanisms
- GANs (Generative Adversarial Networks)
- Autoencoders
MLOps
- Model versioning and tracking
- Experiment management
- Model deployment and serving
- Monitoring and retraining
- CI/CD for ML pipelines
- A/B testing for models
Supervised Learning
```python import numpy as np import pandas as pd from sklearn.model_selection import train_test_split,
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