scaling
Applied AI interview questions tagged scaling, across every topic.
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Concepts behind "scaling"
The curriculum that explains the ideas these questions test.
Foundational
Feature Engineering: Encoding, Scaling, SelectionFeature engineering is the work of turning raw columns into inputs a model can learn from: encoding categoricals, scaling numerics, and selecting which features to keep. Interviews probe it because it is the unglamorous lever that usually moves a metric more than swapping the model, and because the right choice depends on cardinality, the model family, and leakage risk rather than on a default recipe.📊 Evaluation & ML Foundations
Core
Mixture-of-ExpertsA Mixture-of-Experts model replaces the dense feed-forward layer with many expert networks and a router that sends each token to only a few of them. This decouples total parameters (capacity) from per-token compute: the model can be huge while each token activates only a slice. The trade-offs are routing complexity, memory (all experts must be loaded), and load balancing. Applied-AI interviews probe it because most frontier models are MoE, and it explains how models get more capable without proportionally more inference cost.🧠 Foundations of LLMs & GenAISign in