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Jul 25, 2024 10:04 PM
Machine learning (ML) offers powerful tools for building complex prediction systems quickly. However, these quick wins come with hidden costs, often referred to as technical debt, which accumulate and complicate long-term maintenance. This blog post explores the various ways ML systems incur technical debt and provides insights into managing it.

The Concept of Technical Debt in ML

Technical debt, introduced by Ward Cunningham in 1992, refers to the long-term costs incurred by taking shortcuts to achieve quick wins in software engineering. In ML, this debt manifests through the additional maintenance problems and ML-specific issues that go beyond traditional code maintenance. Unlike conventional software, ML systems face unique challenges due to their reliance on data and the environment's dynamic nature.

Key Risk Factors in ML Systems

1. Boundary Erosion

ML systems often blur abstraction boundaries, leading to entanglement where changes in one part of the system impact others unpredictably. This issue, known as the CACE principle (Changing Anything Changes Everything), complicates maintenance and improvement efforts.

2. Correction Cascades

Creating correction models on top of existing models introduces dependencies that make future improvements more expensive and complex. A better approach is to integrate corrections directly into the original model or to develop separate models for new problems.

3. Undeclared Consumers

Models whose outputs are used by other systems without proper access controls create hidden dependencies. This tight coupling can lead to unexpected issues when the model is updated, increasing the difficulty of making changes.

4. Data Dependencies

Unstable data dependencies arise when input signals change over time, often without the model's awareness. These changes can disrupt model performance and are challenging to diagnose. Versioning data inputs can mitigate these risks, but it introduces its own complexities.

5. Feedback Loops

ML systems can influence their own future behavior, creating feedback loops that are difficult to predict and manage. These loops can be direct, where the model influences its training data, or hidden, where multiple systems interact indirectly through the environment.

6. System Anti-Patterns

Common anti-patterns in ML systems include excessive glue code, pipeline jungles, dead experimental codepaths, and abstraction debt. These patterns increase system complexity and hinder future development.

Mitigation Strategies

  1. Isolate Models: Use ensembles to reduce entanglement and improve isolation between models.
  1. Detect Changes: Implement tools to visualize and monitor changes in prediction behavior.
  1. Version Data Inputs: Use versioning to manage unstable data dependencies.
  1. Automate Testing and Monitoring: Develop robust testing and monitoring frameworks to detect issues early and ensure system reliability.
  1. Holistic Data Management: Design data pipelines with a clean-slate approach to reduce complexity and improve maintainability.

My Thoughts

  1. Ramp-Up Speed: Measure the complexity of the ML system by how quickly new team members can become productive, ideally within 2 months.
  1. Feature Development Time: Gauge data pipeline and production efficiency by the time required to develop and integrate a new data feature, with 2-3 weeks as the optimal range.
  1. Productionization Time: If it takes over a month to deploy a newly developed model into production (excluding research and development time), the system is likely too complicated.
  1. Occam's Razor: Regularly apply Occam's Razor to remove unnecessary components and simplify the system.
By recognizing and addressing these hidden debts, teams can improve the maintainability, reliability, and performance of their ML systems, ensuring long-term success and scalability.
 

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