System Designer

🏗️ ML Systems Design

Designing production ML systems requires systematic thinking about business requirements, technical constraints, and architectural patterns. Learn to balance trade-offs between accuracy, latency, cost, and maintainability while building scalable systems.

System Thinking

End-to-end system design approach

Trade-offs

Balance accuracy vs latency decisions

Lifecycle

Manage development to deployment

Scaling

Handle growth and optimization

Design Principles & Framework

System Design Overview

Python

Key Concepts:

• End-to-end system thinking
• Business requirements translation
• Technical constraint evaluation
• Trade-off analysis and decision making

class MLSystemDesign:
    """
    Framework for designing production ML systems
    Following Chip Huyen's systematic approach
    """
    
    def __init__(self, business_objective: str):
        self.business_objective = business_objective
        self.requirements = {}
        self.constraints = {}
        self.architecture = {}
        
    def define_requirements(self):
        """Step 1: Clarify the problem and success metrics"""
        return {
            'business_metrics': [
                'Revenue impact ($$)',
                'User engagement (CTR, time spent)',
                'Cost reduction (operational efficiency)',
                'Risk mitigation (fraud prevention)'
            ],
            'ml_metrics': [
                'Precision/Recall for ranking',
                'BLEU score for translation',
                'Latency (p95 < 100ms)',
                'Throughput (1000 QPS)'
            ],
            'system_requirements': [
                'Availability (99.9% uptime)',
                'Scalability (handle 10x traffic)',
                'Compliance (GDPR, privacy)',
                'Maintainability (easy updates)'
            ]
        }
    
    def analyze_constraints(self):
        """Step 2: Identify technical and business constraints"""
        return {
            'data_constraints': {
                'volume': 'TB scale data processing',
                'velocity': 'Real-time vs batch processing',
                'variety': 'Structured, unstructured, multi-modal',
                'quality': 'Missing data, noise, bias'
            },
            'compute_constraints': {
                'latency': 'Sub-second response requirements',
                'throughput': 'Peak traffic handling',
                'cost': 'Budget limitations for GPUs/TPUs',
                'infrastructure': 'On-premise vs cloud'
            },
            'team_constraints': {
                'expertise': 'ML engineering vs research skills',
                'resources': 'Team size and timeline',
                'maintenance': 'Long-term support capability'
            }
        }
        
    def design_architecture(self):
        """Step 3: High-level system architecture"""
        return {
            'data_layer': {
                'ingestion': 'Kafka, Kinesis, Pub/Sub',
                'storage': 'Data lakes, warehouses, feature stores',
                'processing': 'Spark, Airflow, Kubeflow'
            },
            'ml_layer': {
                'training': 'Distributed training, HPO',
                'serving': 'Model endpoints, batch inference',
                'monitoring': 'Performance, drift detection'
            },
            'application_layer': {
                'apis': 'REST, GraphQL, gRPC',
                'frontend': 'Web, mobile, embedded',
                'integration': 'A/B testing, feature flags'
            }
        }

System Design Framework

🎯 Requirements Analysis

• Business objectives and success metrics
• Performance requirements (latency, throughput)
• Scalability and availability needs
• Compliance and regulatory constraints

⚖️ Constraint Evaluation

• Data availability and quality
• Computational resources and budget
• Team expertise and timeline
• Integration and legacy system compatibility

🏗️ Architecture Design

• Data ingestion and processing pipeline
• Model training and validation infrastructure
• Serving and inference architecture
• Monitoring and observability systems

📊 Implementation Strategy

• Iterative development and MVP approach
• Risk mitigation and fallback plans
• Testing and validation strategies
• Deployment and rollout planning

Common ML System Patterns

Pattern	Use Case	Pros	Cons
Batch Processing	Recommendations, ETL	High throughput, Cost effective	High latency, Not real-time
Online Serving	Search, Fraud detection	Low latency, Real-time	High cost, Complex infrastructure
Stream Processing	Monitoring, Real-time analytics	Continuous processing, Timely insights	Complex debugging, State management
Hybrid Architecture	E-commerce, Social media	Flexible, Best of both worlds	Increased complexity, More moving parts

Design Best Practices

🎯 Start Simple

• Begin with simple baselines and heuristics
• Use existing tools and services when possible
• Implement minimum viable product (MVP) first
• Measure everything and iterate based on data

🔄 Plan for Evolution

• Design modular and loosely coupled components
• Plan for model retraining and updates
• Build in A/B testing and rollback capabilities
• Consider future scaling and feature additions

📊 Emphasize Observability

• Comprehensive logging and monitoring
• Data quality and drift detection
• Performance and business metrics tracking
• Alerting and incident response procedures

🔒 Security & Compliance

• Data privacy and protection measures
• Access control and authentication
• Audit trails and compliance documentation
• Regular security assessments and updates

Essential Technologies for ML Systems Design

🔄

MLflow→

Experiment tracking and model lifecycle management

☸️

Kubernetes→

Container orchestration for scalable ML deployments

⚡

Apache Spark→

Distributed data processing for large-scale ML

🚀

Apache Kafka→

Streaming platform for real-time ML data pipelines

📊

Prometheus→

Monitoring and alerting for ML system observability

🐳

Docker→

Containerization for consistent ML deployments

📝 ML Systems Design Mastery Check

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ML Systems Design