Technical Debt and Sustainability Questions

Covers strategies and practices for managing technical debt while ensuring long term operational sustainability of systems and infrastructure. Topics include identifying and classifying technical debt, prioritization frameworks, balancing refactoring and feature delivery, and aligning remediation with business timelines. Also covers operational concerns such as monitoring, observability, alerting, incident response, on call burden, runbook and lifecycle management, infrastructure investments, and architectural changes to reduce long term cost and risk. Includes engineering practices like test coverage, continuous integration and deployment hygiene, code reviews, automated testing, and incremental refactoring techniques, as well as organizational approaches for coaching teams, defining metrics and dashboards for system health, tracking debt backlogs, and making trade off decisions with product and leadership stakeholders.

EasyTechnical

0 practiced

List and classify common sources of technical debt specific to ML systems. For each category provide practical indicators to monitor (what metrics or symptoms you'd see) and a simple detection method to use on an existing codebase or pipeline to surface that debt.

EasyTechnical

0 practiced

Define SLIs and SLOs for a machine learning service that provides real-time predictions to customers. Give two example SLIs (one latency, one quality), propose numerical SLO targets, and explain how an error budget would guide technical debt remediation and release decisions.

HardTechnical

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You must choose between building an in-house model serving platform or adopting a managed model serving service. Evaluate the decision from the perspective of technical debt, sustainability, long-term cost, control, and testability. Provide criteria that would push you to one choice over the other.

MediumTechnical

0 practiced

Describe CI/CD hygiene and gating you would enforce for ML model artifacts. Include which checks must pass before a model can be promoted to staging and production, how to manage model artifact immutability and versioning, and how to ensure reproducible training runs in the pipeline.

HardTechnical

0 practiced

Create a cost model to compare ongoing training costs versus investing in infrastructure changes (for example caching, distributed training optimization, or spot instances). Explain the inputs to the model, how you would estimate savings, and how cost modeling feeds prioritization of technical debt remediation.

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