Deployment & Beyond

Discussing deployment and monitoring is a crucial signal of real-world machine learning experience in an interview. Here are the core points from this lesson:

• Familiarize yourself with deployment patterns like shadow mode, A/B testing, and blue-green deployments, understanding their trade-offs and when to apply them
• Monitor both offline (ML engineer-focused) and online (business-focused) metrics, paying close attention to the distribution of inputs and outputs at each stage of a multi-stage system
• Recognize that significant shifts in input or output distributions should trigger immediate attention, as they often indicate real-world changes affecting model performance
• Avoid suggesting online learning unless you have deep expertise, as it can lead to complex follow-up questions
• Be prepared to discuss retraining strategies, as models inevitably need updates over time

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