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name: ml-systems-fundamentals description: Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.

ML Systems Fundamentals

Foundation concepts for building production ML systems.

ML System Architecture

Copy┌─────────────────────────────────────────────────────────────┐
│                    ML SYSTEM ARCHITECTURE                    │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  DATA LAYER                                                  │
│  ├── Data Collection    ├── Data Storage                    │
│  ├── Data Processing    └── Feature Store                   │
│                                                              │
│  MODEL LAYER                                                 │
│  ├── Training Pipeline  ├── Experiment Tracking              │
│  ├── Model Registry     └── Evaluation                      │
│                                                              │
│  SERVING LAYER                                               │
│  ├── Model Serving      ├── Feature Serving                 │
│  ├── Prediction Cache   └── Load Balancing                  │
│                                                              │
│  MONITORING LAYER                                            │
│  ├── Data Monitoring    ├── Model Monitoring                │
│  ├── System Metrics     └── Alerting                        │
│                                                              │
└─────────────────────────────────────────────────────────────┘

ML Lifecycle

1. Problem Definition - Business goal → ML task
2. Data Collection - Gather relevant data
3. Data Processing - Clean, transform, validate
4. Feature Engineering - Create informative features
5. Model Development - Train, tune, evaluate
6. Deployment - Serve predictions
7. Monitoring - Track performance
8. Iteration - Improve based on feedback

System Requirements

Reliability

• Handle failures gracefully
• Maintain prediction quality
• Provide consistent latency

Scalability

• Handle growing data
• Support more requests
• Enable parallel training

Maintainability

• Easy to update models
• Clear documentation
• Reproducible experiments

Adaptability

• Respond to data changes
• Support new features
• Enable quick iterations

Design Principles

Copy# 1. Start Simple
baseline = LogisticRegression()
baseline.fit(X_train, y_train)
print(f"Baseline: {baseline.score(X_test, y_test)}")

# 2. Data Quality > Model Complexity
def validate_data(df):
    assert df.isnull().sum().sum() == 0
    assert df.duplicated().sum() == 0
    return True

# 3. Version Everything
import mlflow
mlflow.log_param("model_version", "1.0.0")
mlflow.log_artifact("data/processed/")

# 4. Monitor Continuously
def check_drift(reference, current):
    return ks_2samp(reference, current).pvalue < 0.05

Commands

• /omgml:init - Initialize ML project
• /omgml:status - Project status

Best Practices

1. Define clear success metrics
2. Establish baselines early
3. Invest in data quality
4. Automate everything possible
5. Monitor production models