AI Project Best Practices

Complete guide to AI project success.

Project Phases

Problem definition
Data collection
Exploratory analysis
Model development
Evaluation
Deployment
Monitoring

Phase 1: Define Problem

Ask key questions:

What problem are we solving?

Clear, specific goal
Measurable success criteria

Is AI the right solution?

Need enough data
Problem must be learnable

What's the business impact?

Cost savings
Revenue increase
User experience improvement

Phase 2: Data Strategy

import pandas as pd

# Data checklist
checklist = {
    'quantity': 'At least 1000 samples',
    'quality': 'Clean, accurate labels',
    'relevance': 'Matches real-world use',
    'balance': 'Equal class distribution',
    'privacy': 'Complies with regulations'
}

# Initial data exploration
df = pd.read_csv('data.csv')

print("Shape:", df.shape)
print("
Missing values:")
print(df.isnull().sum())
print("
Class distribution:")
print(df['target'].value_counts())

Phase 3: EDA (Exploratory Data Analysis)

import matplotlib.pyplot as plt
import seaborn as sns

# Distribution plots
fig, axes = plt.subplots(2, 2, figsize=(12, 10))

# Feature distributions
df['feature1'].hist(ax=axes[0, 0])
axes[0, 0].set_title('Feature 1 Distribution')

# Target distribution
df['target'].value_counts().plot(kind='bar', ax=axes[0, 1])
axes[0, 1].set_title('Target Distribution')

# Correlation heatmap
corr = df.corr()
sns.heatmap(corr, annot=True, ax=axes[1, 0])
axes[1, 0].set_title('Correlation Matrix')

# Box plot for outliers
df.boxplot(column='feature1', by='target', ax=axes[1, 1])
axes[1, 1].set_title('Feature by Target')

plt.tight_layout()
plt.show()

Phase 4: Model Development

Start simple, then improve:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Split data
X = df.drop('target', axis=1)
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Baseline model (simple)
baseline = LogisticRegression()
baseline.fit(X_train, y_train)
baseline_score = baseline.score(X_test, y_test)
print(f"Baseline accuracy: {baseline_score:.3f}")

# Advanced model
advanced = RandomForestClassifier(n_estimators=100)
advanced.fit(X_train, y_train)
advanced_score = advanced.score(X_test, y_test)
print(f"Advanced accuracy: {advanced_score:.3f}")

# Detailed metrics
y_pred = advanced.predict(X_test)
print(classification_report(y_test, y_pred))

Phase 5: Proper Evaluation

from sklearn.model_selection import cross_val_score

# Cross-validation
cv_scores = cross_val_score(advanced, X, y, cv=5)
print(f"CV scores: {cv_scores}")
print(f"Mean CV score: {cv_scores.mean():.3f} (+/- {cv_scores.std():.3f})")

# Test on unseen data
# Load fresh test set
test_df = pd.read_csv('new_test_data.csv')
X_new_test = test_df.drop('target', axis=1)
y_new_test = test_df['target']

final_score = advanced.score(X_new_test, y_new_test)
print(f"Final test score: {final_score:.3f}")

Phase 6: Documentation

Create a model card:

model_card = {
    'model_details': {
        'name': 'Customer Churn Predictor',
        'version': '1.0',
        'date': '2025-12-18',
        'type': 'Random Forest Classifier'
    },
    'intended_use': {
        'primary': 'Predict customer churn',
        'out_of_scope': 'Not for credit decisions'
    },
    'performance': {
        'accuracy': 0.87,
        'precision': 0.85,
        'recall': 0.89
    },
    'data': {
        'training_data': '50,000 customer records',
        'features': ['age', 'tenure', 'monthly_charges'],
        'date_range': '2023-2024'
    },
    'limitations': [
        'Works best for USA customers',
        'Accuracy drops for new customers (<3 months)',
        'Requires monthly updates'
    ],
    'ethical_considerations': [
        'No protected attributes used',
        'Regular bias audits',
        'Human review for high-risk decisions'
    ]
}

import json
with open('model_card.json', 'w') as f:
    json.dump(model_card, f, indent=2)

Phase 7: Deployment Checklist

deployment_checklist = {
    'model': {
        'saved': '✓ model.pkl',
        'tested': '✓ Unit tests pass',
        'versioned': '✓ v1.0 in MLflow'
    },
    'api': {
        'endpoint': '✓ /predict created',
        'docs': '✓ Swagger docs',
        'auth': '✓ API key required'
    },
    'infrastructure': {
        'docker': '✓ Dockerfile ready',
        'ci_cd': '✓ GitHub Actions',
        'monitoring': '✓ Prometheus + Grafana'
    },
    'security': {
        'input_validation': '✓ Implemented',
        'rate_limiting': '✓ 100 req/min',
        'https': '✓ SSL certificate'
    }
}

Common Mistakes to Avoid

Data leakage: Test data in training
Overfitting: Model memorizes training data
Wrong metric: Accuracy for imbalanced data
No baseline: Nothing to compare against
Ignoring deployment: Model stuck in notebook
No monitoring: Performance degrades unseen

Project Organization

project/
├── data/
│   ├── raw/
│   ├── processed/
│   └── external/
├── notebooks/
│   ├── 01_exploration.ipynb
│   ├── 02_modeling.ipynb
│   └── 03_evaluation.ipynb
├── src/
│   ├── data/
│   ├── features/
│   ├── models/
│   └── visualization/
├── tests/
├── models/
├── requirements.txt
└── README.md

Success Metrics

Track these:

metrics_to_track = {
    'model_metrics': {
        'accuracy': 0.87,
        'latency_ms': 45,
        'throughput': '1000 req/sec'
    },
    'business_metrics': {
        'revenue_impact': '$50,000/month',
        'cost_savings': '$20,000/month',
        'user_satisfaction': '4.5/5'
    },
    'operational_metrics': {
        'uptime': '99.9%',
        'errors': '0.1%',
        'data_drift': 'None detected'
    }
}

Remember

Start with clear problem definition
Invest time in data quality
Build baseline before complex models
Document everything
Monitor after deployment
Iterate based on feedback
Focus on business value!