Machine Learning Pipeline

What You Need to Learn

1. ML Fundamentals: Supervised vs Unsupervised learning
2. Feature Engineering: Creating meaningful inputs
3. Model Selection: Choosing the right algorithm
4. Training & Validation: Cross-validation, hyperparameter tuning
5. MLOps: Model versioning, monitoring, deployment

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ELI5: What is Machine Learning?

Teaching a computer through examples, not rules:

Traditional Programming:

• You: "If email contains 'FREE MONEY', mark as spam"
• Computer: Follows exact rules

Machine Learning:

• You: "Here are 1000 spam emails and 1000 real emails"
• Computer: "I'll figure out the patterns myself!"

Example: Email Spam Filter

• Shows computer many spam emails (training)
• Computer learns patterns (free, urgent, click here)
• New email arrives → Computer predicts: spam or not

Key Insight: ML finds patterns too complex for humans to write rules for!

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System Design: End-to-End ML Pipeline

┌───────────────────┐
│  Data Collection  │
│  (APIs, DBs)      │
└─────────┬─────────┘
          │
          ▼
┌───────────────────┐
│ Feature Store     │
│ (Databricks, Feast)│
└─────────┬─────────┘
          │
          ▼
┌───────────────────┐
│ Model Training    │
│ (XGBoost, PyTorch)│
└─────────┬─────────┘
          │
          ▼
┌───────────────────┐
│ Model Registry    │
│ (MLflow)          │
└─────────┬─────────┘
          │
          ▼
┌───────────────────┐
│ Model Deployment  │
│ (API, Batch)      │
└─────────┬─────────┘
          │
          ▼
┌───────────────────┐
│ Monitoring        │
│ (Drift Detection) │
└───────────────────┘

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The ML Development Cycle

1. Problem Definition

• What are we predicting?
• What data do we have?
• Success metrics?

2. Data Preparation

# Example: Feature engineering
import pandas as pd

# Create time-based features
df['hour'] = df['timestamp'].dt.hour
df['day_of_week'] = df['timestamp'].dt.dayofweek

# Handle missing values
df['age'].fillna(df['age'].median(), inplace=True)

# Encode categories
df = pd.get_dummies(df, columns=['category'])

3. Model Selection & Training

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

model = RandomForestClassifier(n_estimators=100)
scores = cross_val_score(model, X, y, cv=5)
print(f"Accuracy: {scores.mean():.2f}")

4. Evaluation

• Classification: Accuracy, Precision, Recall, F1, ROC-AUC
• Regression: RMSE, MAE, R²
• Always use validation set, never test set during development!

5. Deployment

• Batch predictions vs Real-time API
• Model versioning (MLflow)
• A/B testing

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Common Pitfalls & Solutions

Data Leakage

Problem: Using future information to predict the past Solution: Strict train/test split based on time

Overfitting

Problem: Model memorizes training data Solution: Cross-validation, regularization, simpler models

Imbalanced Data

Problem: 99% class A, 1% class B Solution: SMOTE, class weights, proper metrics (F1, not accuracy)

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Real-World Example

At Capital One, I built fraud detection:

• Data: 10M+ transactions, 0.1% fraud rate
• Approach:
  • Time-series features (spending patterns)
  • Isolation Forest for anomaly detection
  • XGBoost for classification
• Result: 90% fraud detection accuracy, real-time scoring

At UNT, energy forecasting:

• LSTM networks for time-series prediction
• 15% improvement over baseline
• SHAP for interpretability

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Best Practices

1. Start Simple: Baseline model first (logistic regression)
2. Feature Engineering > Complex Models: Good features beat fancy algorithms
3. Monitor in Production: Track model drift
4. Version Everything: Data, code, models
5. Document Assumptions: Make ML reproducible