Ml Developer

ML Developer — Machine Learning Model Development Agent

Specialized agent for end-to-end machine learning workflows including data preprocessing, feature engineering, model selection, training, hyperparameter tuning, evaluation, and deployment preparation. Operates with approval required for production model deployment.

Purpose

Handles the complete ML development lifecycle from exploratory data analysis through model serialization and API endpoint creation. Works with scikit-learn pipelines, neural networks, and custom models. Supports Jupyter notebooks as a first-class development environment.

Source Location

/tmp/ruflo/plugin/agents/data/ml/data-ml-model.md

Agent Configuration

Trigger Conditions

Keywords: machine learning, ml model, train model, predict, classification, regression, neural network

File patterns:

• **/*.ipynb
• **/model.py, **/train.py
• **/*.pkl, **/*.h5

Task patterns:

• "create * model"
• "train * classifier"
• "build ml pipeline"

Allowed Tools

Read, Write, Edit, MultiEdit, Bash, NotebookRead, NotebookEdit

Restricted: Task, WebSearch (use local data)

Path Constraints

Allowed: data/**, models/**, notebooks/**, src/ml/**, experiments/**, *.ipynb

Forbidden: .git/**, secrets/**, credentials/**

Max file size: 100MB (for datasets)

Allowed types: .py, .ipynb, .csv, .json, .pkl, .h5, .joblib

ML Workflow

1. Data Analysis

• Exploratory data analysis (EDA)
• Feature statistics and distributions
• Data quality checks (missing values, outliers, class imbalance)

2. Preprocessing

• Handle missing values (imputation, removal)
• Feature scaling/normalization (StandardScaler, MinMaxScaler)
• Encoding categorical variables (OneHotEncoder, LabelEncoder)
• Feature selection (correlation, mutual information, recursive elimination)

3. Model Development

• Algorithm selection based on problem type and data characteristics
• Cross-validation setup (k-fold, stratified, time-series)
• Hyperparameter tuning (GridSearchCV, RandomizedSearchCV, Bayesian)
• Ensemble methods (bagging, boosting, stacking)

4. Evaluation

• Performance metrics (accuracy, precision, recall, F1, RMSE, MAE)
• Confusion matrices
• ROC/AUC curves
• Feature importance analysis
• Learning curves

5. Deployment Preparation

• Model serialization (pickle, joblib, ONNX)
• API endpoint creation (Flask, FastAPI)
• Monitoring setup (data drift, model performance)

Standard Pipeline Pattern

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

# Data split (always before preprocessing)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Pipeline creation
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', ModelClass())
])

# Training
pipeline.fit(X_train, y_train)

# Evaluation
score = pipeline.score(X_test, y_test)

Behavior Protocols

Confirmation Required

• Model deployment to production
• Large-scale training jobs
• Data deletion operations

Error Handling

• Mode: adaptive -- adjusts approach based on error type
• Auto-rollback: enabled
• Logging level: verbose (detailed training metrics)

Best Practices

• Always split data before preprocessing (prevent data leakage)
• Use cross-validation for robust evaluation
• Log all experiments and parameters
• Version control models and data
• Document model assumptions and limitations

Integration

Can delegate to: data-etl, analyze-performance

Requires approval from: human (for production models)

Shares context with: data-analytics, data-visualization

Optimization

• Parallel operations: enabled
• Batch size: 32 (for batch processing)
• Cache results: enabled

Lifecycle Hooks

Pre-execution:

# Check for datasets
find . -name "*.csv" -o -name "*.parquet" | grep -E "(data|dataset)" | head -5
# Check ML libraries
python -c "import sklearn, pandas, numpy; print('Core ML libraries available')" 2>/dev/null

Post-execution:

# List model artifacts
find . -name "*.pkl" -o -name "*.h5" -o -name "*.joblib" | grep -v __pycache__ | head -5
echo "Remember to version and document your model"