Building Scalable ML Infrastructure

January 15, 2024

When I joined Ideal as a Machine Learning Engineer, I inherited a monolithic ML application that was becoming increasingly difficult to maintain and scale. The system processed thousands of inference requests daily, but deployment was slow, debugging was painful, and adding new models required significant architectural changes.

Over the course of 18 months, we successfully transitioned this monolith to a microservices architecture, reducing AWS costs by 20% while dramatically improving our development velocity. Here's what I learned from that experience.

The Problem with Monolithic ML Systems

Our original system had several pain points that are common in monolithic ML applications:

  • Deployment bottlenecks: Any change to a single model required redeploying the entire application
  • Resource inefficiency: All models shared the same compute resources, leading to over-provisioning for peak loads
  • Scaling challenges: We couldn't scale individual models based on their specific usage patterns
  • Technology lock-in: All models had to use the same framework and dependencies

The Microservices Approach

We decided to break the monolith into individual microservices, with each service responsible for a specific model or closely related group of models. This approach offered several advantages:

  • Independent deployment: Teams could deploy model updates without affecting other services
  • Technology flexibility: Each service could use the most appropriate framework and dependencies
  • Granular scaling: We could scale services based on individual demand patterns
  • Fault isolation: Issues with one model wouldn't bring down the entire system

Implementation Strategy

The transition wasn't done overnight. We used a strangler fig pattern, gradually extracting services from the monolith:

# Example service structure ml-service-template/ ├── src/ │ ├── model/ │ │ ├── __init__.py │ │ └── predictor.py │ ├── api/ │ │ ├── __init__.py │ │ └── routes.py │ └── utils/ ├── tests/ ├── Dockerfile ├── requirements.txt └── deploy.yml

Each service followed a standardized template that included:

  • Consistent API interfaces using FastAPI
  • Standardized logging and monitoring
  • Health check endpoints
  • Automated testing and deployment pipelines

Caching Strategy Overhaul

One of the biggest performance improvements came from rethinking our caching strategy. The original Redis-based cache was becoming a bottleneck, so we moved to AWS S3 for model artifact storage with intelligent caching layers.

This change alone improved our inference times significantly and reduced the operational overhead of managing Redis clusters.

Results and Lessons Learned

The transition took about 8 months and delivered measurable improvements:

  • 20% reduction in AWS costs through better resource utilization
  • 50% faster deployment times for individual models
  • Improved system reliability with better fault isolation
  • Faster onboarding for new team members with standardized templates

However, the transition also introduced new challenges:

  • Increased operational complexity with more services to monitor
  • Network latency considerations for service-to-service communication
  • More sophisticated deployment orchestration requirements

Key Takeaways

If you're considering a similar transition, here are my key recommendations:

  • Start with templates: Create standardized service templates before you begin extracting services
  • Invest in monitoring: Distributed systems require more sophisticated observability
  • Plan for data consistency: Think carefully about how services will share and synchronize data
  • Automate everything: The operational overhead of microservices makes automation essential

Have questions about ML infrastructure or want to share your own experiences? Feel free to reach out at matt@emmons.club.

© 2025 Matt Emmons