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About Machine Learning Interviews
This document provides an overview of designing machine learning systems for interviews. It discusses key differences between research and production settings, including performance requirements, compute requirements, and goals. The document outlines the main components of designing a machine learning system: project setup, data pipeline, modeling (model selection, training, debugging, hyperparameter tuning, scaling), and serving. It provides examples of iterative design where steps may need to be revisited based on results. The goal is to help candidates structure their approach when asked open-ended design questions in interviews.
Detailed Information
| Author: | ['Mark Fisher'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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