Seemed basic at first and I almost relaxed too much.
Start by defining both training and inference clearly, then contrast them across key dimensions like computational requirements, data flow, and objectives. Emphasize that training is a one-time (or periodic) heavy process to learn parameters, while inference is a lightweight, repeated process to apply those parameters to new data. Use examples to illustrate the differences and highlight trade-offs relevant to system design at scale.
Pro tip: At Amazon, interviewers value practical insights: mention how inference often requires optimization for latency and cost, while training focuses on throughput and convergence, and discuss how this impacts system architecture decisions.
Explain that training involves feeding labeled data through a model to learn parameters (weights and biases) by minimizing a loss function, typically using backpropagation and gradient descent.
Describe inference as using the trained model to make predictions on new, unseen data, where the parameters are fixed and no learning occurs.
Contrast the computational intensity: training requires significant resources (GPUs/TPUs, memory, time) for forward and backward passes, while inference is lighter, often optimized for low latency and high throughput.
Highlight that training uses large labeled datasets and aims to generalize well, whereas inference uses individual or small batches of unlabeled data and aims to produce accurate, fast predictions.
Explain how these differences influence system design: training may be done offline in batches, while inference is often online and real-time, requiring different infrastructure, scaling, and optimization strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.