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Case 1

meetup presentation

Build an ML Product – 4 Mistakes to Avoid

If you want to build an industry-grade ML product, you have to avoid the 4 most common mistakes. Here, I describe these common mistakes and provide suggestions to avoid them. If your answers to the below question are negative, you may want to think twice.

  • Is mismatch in user-test data important?
  • Are problem-specific metrics required?
  • Is a single ML model necessarily appropriate?
  • Is the model archiving important?