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?