Section 01
ML System Architecture Fundamentals
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From Notebook to Production
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Batch vs. Online, Not Both at Once
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A Safe Path to Raw Data
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A Small, Complete ML Lifecycle
Section 02
Data Collection & Labeling
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Sampling Bias in Event Collection
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Joining Late-Arriving Labels
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Human Labeling Throughput
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Filtering Sensitive Data Before Training
Section 03
Data Pipelines & Preprocessing
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Outgrowing the Nightly Window
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A Realtime Feature Arrives Too Late
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Duplicate Events Corrupt Training Examples
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Backfills Overwhelm the Pipeline
Section 04
Feature Engineering & Feature Stores
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Training-Serving Skew
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Online Feature Lookups Under Peak Load
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Freshness SLAs Differ by Feature
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