Lesson 19: ML Pipeline Integration - Training & Serving Data

Data Platform & Analytics Architecture

Part 6: ML & Data Platform

xdev.asia

Introduction

Data platform for ML: training data preparation, labeling pipelines. Model training data versioning (DVC). A/B testing data. Experimental tracking.

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1. Data platform for ML: training data preparation, labeling pipelines

1.1 Basic concepts

Data platform for ML: training data preparation, labeling pipelines are one of the most important topics in this field. Understanding the core concepts will help you design the right system from the beginning.

Key Concepts:
├── Concept 1: Nền tảng lý thuyết
├── Concept 2: Áp dụng thực tế
├── Concept 3: Best practices
└── Concept 4: Anti-patterns cần tránh

1.2 Why is it important?

Aspect	Not applicable	Correct application
Performance	Bottlenecks, high latency	Optimized, scalable
Reliability	Single point of failure	Fault-tolerant
Maintainability	Technical debt accumulated	Clean architecture
Security	Vulnerable	Defense in depth

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2. Model training data versioning (DVC)

2.1 General architecture

┌─────────────────────────────────────────────────────┐
│                  SYSTEM ARCHITECTURE                 │
│                                                      │
│  ┌──────────┐  ┌──────────┐  ┌──────────────────┐  │
│  │  Client   │  │  API     │  │  Core Service    │  │
│  │  Layer    │──│  Gateway │──│  Layer           │  │
│  └──────────┘  └──────────┘  └──────────────────┘  │
│                                      │               │
│                               ┌──────▼──────┐       │
│                               │  Data Layer │       │
│                               └─────────────┘       │
└─────────────────────────────────────────────────────┘

2.2 Component Design

Each component in the system needs to be designed with the following principles:

• Single Responsibility: Each component only takes on one responsibility
• Loose Coupling: Minimize dependencies between components
• High Cohesion: Related elements are in the same component
• Interface Segregation: Clear, separate API

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3. A/B testing data

3.1 Design Patterns applied

Applied Patterns:
├── Strategy Pattern: Cho phép thay đổi algorithm at runtime
├── Observer Pattern: Event notification mechanism
├── Repository Pattern: Data access abstraction
└── Factory Pattern: Object creation flexibility

3.2 Code Example

// Example implementation
public interface Service {
    Result process(Request request);
    boolean supports(RequestType type);
}

@Component
public class CoreService implements Service {

    @Override
    public Result process(Request request) {
        // Validate input
        validator.validate(request);

        // Execute business logic
        var result = businessLogic.execute(request);

        // Publish domain event
        eventBus.publish(new ProcessedEvent(result));

        return result;
    }
}

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4. Experimental tracking.

4.1 Monitoring & Observability

Observability Stack:
├── Metrics: Prometheus + Grafana
├── Logging: ELK / Loki
├── Tracing: OpenTelemetry + Jaeger
└── Alerting: PagerDuty

4.2 Performance Optimization

Metrics	Target	Strategy
Latency p99	< 100ms	Caching, async processing
Throughput	> 10K RPS	Horizontal scaling
Availability	99.99%	Multi-region, failover
Error rate	< 0.01%	Circuit breaker, retry

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Summary

In this lesson, we learned about ML Pipeline Integration - Training & Serving Data. Key takeaways:

• Understand core concepts and how to apply
• Design architecture in accordance with requirements
• Implementation patterns and best practices
• Production considerations: monitoring, performance, security

Next article: We will continue with the next topic in the series.