← 📊 Evaluation & ML Foundations
Core
The Computer Vision Pipeline
A production CV system is a chain: ingest and version images, preprocess and augment, fine-tune a pretrained backbone, attach a task head, evaluate with sliced metrics, post-process, then serve and monitor. The invariant that divides working systems from broken ones is train/serve consistency: the exact resize, color space, and normalization have to match at training and inference. AI, ML, and GenAI engineer interviews probe this because most CV failures live at the preprocessing seam, not in the architecture.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceWalk through an end-to-end computer vision pipeline from raw images to a deployed model.→Machine Learning & Data ScienceHow do you approach feature engineering, encoding categoricals, and handling missing data?→ML Infrastructure & GPUsWhat do model-serving frameworks (Triton, TorchServe, vLLM, TGI) provide, and how do you choose?→MLOps & ML EngineeringWhat should you monitor for an ML model in production (beyond uptime)?→MLOps & ML EngineeringYour ground-truth labels arrive weeks late. How do you monitor the model in the meantime?→Behavioral & Project Deep-DivesTell me about a time you pushed back on a request or said no to a stakeholder.→
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