MLOps Best Practices for LLM Applications

MLOps for LLMs

MLOps practices for LLM applications differ from traditional machine learning. LLMs introduce unique challenges around prompt management, versioning, and deployment that require specialized approaches.

Prompt Management

Version Control

Treat prompts as code:

• Version control all prompts
• Use templates with variables
• Track prompt changes over time
• Test prompts before deployment

Prompt Testing

Implement comprehensive prompt testing:

def test_prompt(prompt_template, test_cases):
    results = []
    for case in test_cases:
        filled_prompt = prompt_template.format(**case.inputs)
        response = llm.generate(filled_prompt)
        results.append({
            'case': case.name,
            'expected': case.expected,
            'actual': response,
            'match': evaluate(response, case.expected)
        })
    return results

Deployment Pipelines

Staging Environments

Use multiple environments:

• Development: For experimentation
• Staging: For integration testing
• Production: For live traffic

Canary Deployments

Gradually roll out changes:

1. Deploy to small percentage of traffic
2. Monitor metrics closely
3. Gradually increase if metrics look good
4. Roll back if issues detected

Monitoring and Observability

Key Metrics

• Latency: p50, p95, p99 response times
• Error Rates: Failed requests, timeouts
• Cost: Token usage, API costs
• Quality: Response quality scores

Logging

Comprehensive logging is essential:

• Log all prompts and responses
• Track user interactions
• Monitor for anomalies
• Enable debugging when needed

Model Versioning

Model Registry

Track model versions and their performance:

• Model version identifiers
• Performance metrics per version
• Deployment history
• Rollback capabilities

Testing Strategies

Unit Testing

Test individual components:

• Prompt formatting
• Response parsing
• Error handling

Integration Testing

Test end-to-end flows:

• Full request/response cycles
• Error scenarios
• Edge cases

Regression Testing

Prevent quality degradation:

• Maintain test datasets
• Run tests before deployment
• Track quality over time

CI/CD for LLMs

Automated Pipelines

Build CI/CD pipelines that:

• Run tests automatically
• Validate prompts
• Deploy to staging
• Run smoke tests
• Deploy to production

Rollback Strategies

Plan for quick rollbacks:

• Keep previous versions available
• Implement feature flags
• Monitor closely after deployment
• Have rollback procedures documented

Best Practices

• Treat prompts as production code
• Monitor everything
• Test thoroughly before deployment
• Document your processes
• Plan for failures

Conclusion

MLOps for LLMs requires adapting traditional practices to the unique characteristics of language models. By implementing proper versioning, testing, and monitoring, you can deploy LLM applications with confidence.