We've all seen the dismal and (at this point, annoying) charts and graphs of '>90.x% of ML projects fail' used as marketing ploys by various companies. What this largely simplified view of ML project success rates buries in misleading abstraction is the fact that some companies have a 100% success rate with long-running ML projects while others have a 0% success rate.
This talk is intending to go through a simple concept that is obvious to the 100% success rate companies but is a mystery to those that fail time and again. Firstly, that a project is not an island. It has dependencies on other teams (both technical and non-technical), that the DS team doesn't need to be heroic in pursuing the most complex solution, and how establishing solid engineering practices is what will set apart the projects that will succeed and those that will fail.
The main points that will be covered:
- Can you really solve this with ML? Should you?
- Make sure you have the data consistently and that's it's not garbage (feature stores are great!)
- Start simple and only add complexity if you need to
- Involve the business (SMEs)
- Build code that your team can maintain and test
- Monitor your data and predictions so you know when things are about to break
