Machine Learning Intermediate Pro

Model Serving & Deployment

Choose an inference mode, meet a latency budget, and roll a model out without breaking production

28 min read 9 views

Learn to design the serving side of an ML system: batch vs online vs streaming vs hybrid inference, latency budgets with numbers, model server patterns, compression, canary and shadow rollouts, feature-store consistency, autoscaling, and safe fallbacks.

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Practice questions (5)

Choosing an Inference Mode for a Notification Ranker

Intermediate · Free

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Latency Budget With Fan-Out

Advanced

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Canary, Shadow and A/B for a New Ranker

Intermediate

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Compressing a Ranker for a Tighter Latency Budget

Intermediate

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Capacity Planning for a Ranking Service

Intermediate

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Quizzes (1)

Model Serving & Deployment Quiz

0 attempts · 10 min · Pass 70% · Intermediate

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