ML Expert - Machine Learning Model Development

Overview

Specialized workflow for ML model development, training, and deployment. Supports various architectures (CNNs, RNNs, Transformers) with distributed training capabilities.

When to Use

• Developing new ML models
• Training neural networks
• Model optimization
• Production deployment
• Transfer learning
• Fine-tuning existing models

Phase 1: Data Preparation (10 min)

Objective

Clean, preprocess, and prepare training data

Agent: ML-Developer

Step 1.1: Load and Analyze Data

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

# Load data
data = pd.read_csv('dataset.csv')

# Analyze
analysis = {
    'shape': data.shape,
    'columns': data.columns.tolist(),
    'dtypes': data.dtypes.to_dict(),
    'missing': data.isnull().sum().to_dict(),
    'stats': data.describe().to_dict()
}

# Store analysis
await memory.store('ml-expert/data-analysis', analysis)

Step 1.2: Data Cleaning

# Handle missing values
data = data.fillna(data.mean())

# Remove duplicates
data = data.drop_duplicates()

# Handle outliers
from scipy import stats
z_scores = np.abs(stats.zscore(data.select_dtypes(include=[np.number])))
data = data[(z_scores < 3).all(axis=1)]

# Encode categorical variables
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
for col in data.select_dtypes(include=['object']).columns:
    data[col] = le.fit_transform(data[col])

Step 1.3: Split Data

# Split into train/val/test
X = data.drop('target', axis=1)
y = data['target']

X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)

# Normalize
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_val = scaler.transform(X_val)
X_test = scaler.transform(X_test)

# Save preprocessed data
np.save('data/X_train.npy', X_train)
np.save('data/X_val.npy', X_val)
np.save('data/X_test.npy', X_test)

Validation Criteria

• Data loaded successfully
• Missing values handled
• Train/val/test split created
• Normalization applied

Phase 2: Model Selection (10 min)

Objective

Choose and configure model architecture

Agent: Researcher

Step 2.1: Analyze Task Type

task_analysis = {
    'type': 'classification|regression|clustering',
    'complexity': 'low|medium|high',
    'dataSize': len(data),
    'features': X.shape[1],
    'classes': len(np.unique(y)) if classification else None
}

# Recommend architecture
if task_analysis['type'] == 'classification' and task_analysis['features'] > 100:
    recommended_architecture = 'deep_neural_network'
elif task_analysis['type'] == 'regression' and task_analysis['dataSize'] < 10000:
    recommended_architecture = 'random_forest'
# ... more logic

Step 2.2: Define Architecture

import tensorflow as tf

def create_model(architecture='dnn', input_shape, num_classes):
    if architecture == 'dnn':
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(256, activation='relu', input_shape=input_shape),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(128, activation='relu'),
            tf.keras.layers.Dropout(0.3),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(num_classes, activation='softmax')
        ])
    elif architecture == 'cnn':
        model = tf.keras.Sequential([
            tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=input_shape),
            tf.keras.layers.MaxPooling2D((2,2)),
            tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
            tf.keras.layers.MaxPooling2D((2,2)),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(128, activation='relu'),
            tf.keras.layers.Dense(num_classes, activation='softmax')
        ])
    # ... more architectures

    return model

model = create_model('dnn', input_shape=(X_train.shape[1],), num_classes=len(np.unique(y)))
model.summary()

Step 2.3: Configure Training

training_config = {
    'optimizer': tf.keras.optimizers.Adam(learning_rate=0.001),
    'loss': 'sparse_categorical_crossentropy',
    'metrics': ['accuracy'],
    'batch_size': 32,
    'epochs': 100,
    'callbacks': [
        tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True),
        tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5),
        tf.keras.callbacks.ModelCheckpoint('best_model.h5', save_best_only=True)
    ]
}

model.compile(
    optimizer=training_config['optimizer'],
    loss=training_config['loss'],
    metrics=training_config['metrics']
)

Validation Criteria

• Task analyzed
• Architecture selected
• Model configured
• Training parameters set

Phase 3: Train Model (20 min)

Objective

Execute training with monitoring

Agent: ML-Developer

Step 3.1: Start Training

# Train model
history = model.fit(
    X_train, y_train,
    validation_data=(X_val, y_val),
    batch_size=training_config['batch_size'],
    epochs=training_config['epochs'],
    callbacks=training_config['callbacks'],
    verbose=1
)

# Save training history
import json
with open('training_history.json', 'w') as f:
    json.dump({
        'loss': history.history['loss'],
        'val_loss': history.history['val_loss'],
        'accuracy': history.history['accuracy'],
        'val_accuracy': history.history['val_accuracy']
    }, f)

Step 3.2: Monitor Training

import matplotlib.pyplot as plt

# Plot training curves
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))

# Loss
ax1.plot(history.history['loss'], label='Train Loss')
ax1.plot(history.history['val_loss'], label='Val Loss')
ax1.set_xlabel('Epoch')
ax1.set_ylabel('Loss')
ax1.legend()
ax1.grid(True)

# Accuracy
ax2.plot(history.history['accuracy'], label='Train Accuracy')
ax2.plot(history.history['val_accuracy'], label='Val Accuracy')
ax2.set_xlabel('Epoch')
ax2.set_ylabel('Accuracy')
ax2.legend()
ax2.grid(True)

plt.savefig('training_curves.png')

Step 3.3: Distributed Training (Optional)

# Using Flow-Nexus for distributed training
from flow_nexus import DistributedTrainer

trainer = DistributedTrainer({
    'cluster_id': 'ml-training-cluster',
    'num_nodes': 4,
    'strategy': 'data_parallel'
})

# Train across multiple nodes
trainer.fit(
    model=model,
    train_data=(X_train, y_train),
    val_data=(X_val, y_val),
    config=training_config
)

Validation Criteria

• Training completed
• No NaN losses
• Validation metrics improving
• Model checkpoints saved

Phase 4: Validate Performance (10 min)

Objective

Evaluate model on test set

Agent: Tester

Step 4.1: Evaluate on Test Set

# Load best model
best_model = tf.keras.models.load_model('best_model.h5')

# Evaluate
test_loss, test_accuracy = best_model.evaluate(X_test, y_test, verbose=0)

# Predictions
y_pred = best_model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)

# Detailed metrics
from sklearn.metrics import classification_report, confusion_matrix

metrics = {
    'test_loss': float(test_loss),
    'test_accuracy': float(test_accuracy),
    'classification_report': classification_report(y_test, y_pred_classes, output_dict=True),
    'confusion_matrix': confusion_matrix(y_test, y_pred_classes).tolist()
}

await memory.store('ml-expert/metrics', metrics)

Step 4.2: Generate Evaluation Report

report = f"""
# Model Evaluation Report

## Performance Metrics
- Test Loss: {test_loss:.4f}
- Test Accuracy: {test_accuracy:.4f}

## Classification Report
{classification_report(y_test, y_pred_classes)}

## Model Summary
- Architecture: {recommended_architecture}
- Parameters: {model.count_params()}
- Training Time: {training_time} seconds

## Training History
- Best Val Loss: {min(history.history['val_loss']):.4f}
- Best Val Accuracy: {max(history.history['val_accuracy']):.4f}
- Epochs Trained: {len(history.history['loss'])}
"""

with open('evaluation_report.md', 'w') as f:
    f.write(report)

Validation Criteria

• Test accuracy > target threshold
• No overfitting detected
• Metrics documented
• Report generated

Phase 5: Deploy to Production (15 min)

Objective

Package model for deployment

Agent: ML-Developer

Step 5.1: Export Model

# Save in multiple formats
model.save('model.h5')  # Keras format
model.save('model_savedmodel')  # TensorFlow SavedModel
# Convert to TFLite for mobile
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

# Save preprocessing pipeline
import joblib
joblib.dump(scaler, 'scaler.pkl')

Step 5.2: Create Deployment Package

import shutil
import os

# Create deployment directory
os.makedirs('deployment', exist_ok=True)

# Copy necessary files
shutil.copy('model.h5', 'deployment/')
shutil.copy('scaler.pkl', 'deployment/')
shutil.copy('evaluation_report.md', 'deployment/')

# Create inference script
inference_script = '''
import tensorflow as tf
import joblib
import numpy as np

class ModelInference:
    def __init__(self, model_path, scaler_path):
        self.model = tf.keras.models.load_model(model_path)
        self.scaler = joblib.load(scaler_path)

    def predict(self, input_data):
        # Preprocess
        scaled = self.scaler.transform(input_data)
        # Predict
        predictions = self.model.predict(scaled)
        return np.argmax(predictions, axis=1)

# Usage
inference = ModelInference('model.h5', 'scaler.pkl')
result = inference.predict(new_data)
'''

with open('deployment/inference.py', 'w') as f:
    f.write(inference_script)

Step 5.3: Generate Documentation

# Model Deployment Guide

## Files
- `model.h5`: Trained Keras model
- `scaler.pkl`: Preprocessing scaler
- `inference.py`: Inference script

## Usage
\`\`\`python
from inference import ModelInference

model = ModelInference('model.h5', 'scaler.pkl')
predictions = model.predict(new_data)
\`\`\`

## Performance
- Latency: < 50ms per prediction
- Accuracy: ${test_accuracy}
- Model Size: ${model_size}MB

## Requirements
- tensorflow>=2.0
- scikit-learn
- numpy

Validation Criteria

• Model exported successfully
• Deployment package created
• Inference script tested
• Documentation complete

Success Metrics

• Test accuracy meets target
• Training converged
• No overfitting
• Production-ready deployment

Skill Completion

Outputs:

1. model.h5: Trained model file
2. evaluation_report.md: Performance metrics
3. deployment/: Production package
4. training_history.json: Training logs

Complete when model deployed and validated.