ml-systems-fundamentals

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

┌─────────────────────────────────────────────────────────────┐
│                    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

# 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