- Code Credit - I have observed people take code from git/forums and change headers/rewrite methods without providing due credit. I call out explicitly the reference/approach from paper/presentation/tech talks. The original work would have taken months to arrive at it. It is better to reference it for anyone to understand the reasons behind the design
- Domain Knowledge - Ignoring the value of domain knowledge - To find a possible loss item, there is a mix of techniques in Retail employed. Implementing the same as events/actions may be the dumbest way for someone who undervalues domain knowledge :)
- Code Management - Treating ML code as Enterprise Software - Models are deployed without source code on edge devices. We use disparate sources of data. My recommendation is to build a high accuracy model and then consider merging the code into a common code / common class.
- Resellers - Do not get carried away with third-party tools, frameworks. Try with open source, build your datasets, better models. Buying from the third party AI software and re-selling it we cannot call it as 'Analytics Company' :)
- ROI - Set realistic expectations, target the low hanging fruits, understand ML as 'Preparedness'. ML cannot be quantified in terms of revenue unless it is a clear business case like Market Basket Analysis, Sold together, Bought together
Keep Thinking!!!
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