In real-world ML, data is often more important than the model.
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Before jumping into algorithms, you must define what "success" looks like. In real-world ML
Latency requirements (online vs. offline), data privacy (GDPR), and throughput. data privacy (GDPR)
Where does the data come from? (User logs, relational databases, third-party APIs).