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Join a Research Engineer in computer vision, as he tackles a machine learning mock interview focusing on creating an estimated time of arrival system for a maps application. We explore the challenges of designing a system that calculates ETAs by considering road segments, traffic data, and machine learning models. This interview covers various aspects, including the importance of data quality, model parameterization, training, validation, and deployment strategies.

Chapters (Powered by ChapterMe) -
00:00 - Intro
00:52 - Machine learning system design interview overview
03:50 - Road data prediction with low latency, high performance
05:49 - Key ML sections data setup, training, validation
06:47 - High-level planning for data hygiene, usable data, validation
10:16 - Data map, travel, segment ID
14:46 - Data cleanliness and automated filter implementation
16:58 - Field checking, random sample, human verification
19:06 - Model interface, function, input, time
22:05 - V1 model with parametrization and learnable parameters
26:31 - New downstream table with interval
29:58 - Offline data processing for timeseries analysis
33:17 - Validation and train valve split
42:16 - Metric, aggregation, validation
44:27 - Deployment simple, high-performance, ETA backend
47:06 - Machine learning integration with other components
50:36 - Simplify setup with data flow diagram
53:59 - Resolving outliers in data, validation for models
55:39 - End user experience metrics for product evaluation

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