# Simple MLOps pipeline with MLflow and Docker
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

# Load data
df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']

# Train model with MLflow tracking
with mlflow.start_run():
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X, y)
    
    # Log model
    mlflow.sklearn.log_model(model, "model")
    
    # Log parameters
    mlflow.log_param("n_estimators", 100)
    
    # Log metrics
    mlflow.log_metric("accuracy", model.score(X, y))
    
    # Save model
    mlflow.sklearn.save_model(model, "model_output")

# This is the foundation of MLOps.
# AI Engineers focus on getting models to production.

# End-to-end model training pipeline
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.preprocessing import StandardScaler

# Load and preprocess data
df = pd.read_csv('data.csv')
X = df.drop('target', axis=1)
y = df['target']

# Feature engineering
X['feature_ratio'] = X['feature1'] / X['feature2']
X['log_feature'] = np.log(X['feature3'] + 1)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train and evaluate
model = RandomForestClassifier(n_estimators=200, max_depth=10)
model.fit(X_train_scaled, y_train)

# Evaluate
predictions = model.predict(X_test_scaled)
print(classification_report(y_test, predictions))

# Feature importance
feature_importance = pd.DataFrame({
    'feature': X.columns,
    'importance': model.feature_importances_
}).sort_values('importance', ascending=False)
print(feature_importance)