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Model Selection for Enterprise Deployments

Choosing a model in a customer environment is rarely a benchmark question. Deployment location, data handling terms, procurement status and version stability usually eliminate most of the field before quality enters the conversation, and the model that wins is the best one inside the constraints rather than the best one available.

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RELATED CONCEPTS

Fine-tuning vs RAG vs Prompting Model Routing and Cascades Data Residency and Sovereignty AI Cost and Unit Economics Benchmarks and Their Limits

LESSONS THAT TEACH THIS

Hard DeploymentsHardware arrives on a procurement cycle and your estimate arrives in a meeting30 min→Hard DeploymentsReconcile the bill to attempts, completed work and shared costs30 min→

PRACTICE THIS IN REAL QUESTIONS

Machine Learning & Data ScienceExplain the bias-variance tradeoff, and how it shows up in a real customer deployment.→ML System Design (Product)Design an evaluation framework for an ads-ranking system.→System Design & Production EngineeringIt's Friday evening at a customer site and they want a hotfix shipped now. How do you do it safely, and when do you refuse?→Machine Learning & Data ScienceYour model scored 95% in the pilot and 70% in production. What happened?→MLOps & ML EngineeringWhat's the difference between shadow deployment and A/B testing a model?→Machine Learning & Data ScienceExplain k-fold cross-validation, and when would you refuse to use it?→