ML Design and Coding Question - Design an ML Model To Predict App Deletion (Full mock interview)
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In this interview, we sit down with a former Pinterest Machine Learning Engineer, who shows us the intricate process of creating a model to predict app deletions within 10 weeks. He outlines a comprehensive strategy involving analysis of user data, app usage patterns, and contextual factors, emphasizing the importance of accuracy and privacy in crafting a scalable, globally applicable solution.
Chapters (Powered by ChapterMe) -
00:00 - Introduction to Designing ML System for Predicting App Deletion
00:49 - Overview of Predictive Model Design
03:38 - Key Features for Prediction: Context, User Demographics
05:42 - Trends in App Usage and Interaction Patterns
12:04 - Predictive Model for App Deletions Explained
14:10 - Addressing Missing Features in Model Prediction
20:07 - Understanding Metrics and Calibration in Binary Classification
28:54 - Data Processing and Model Architecture Overview
33:04 - Custom Dataset Handling and Feature Mapping Techniques
44:09 - PyTorch Dataset Implementation and Handling Missing Features
53:38 - PyTorch Model Development with Normalization Techniques
59:47 - Python Training Loop Essentials
01:03:54 - Deep Dive into Gradient Descent and Model Calibration
01:15:12 - Setting Up Data Loader for Efficient Training
01:20:29 - Key Insights on Building Effective ML Models
01:27:30 - Using GBDTs as Baseline for Model Comparison
01:32:50 - Strategic Considerations for Consumer-Focused Companies
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