The high-level steps engaging AI and ML to specific problems.
A roadmap for find tricky solutions to business problems
PROBLEM SOLVING WITH AI
“NOODLING ABOUT WITH DATA”
1. The problem
2. Your theory
This is the problem (or perhaps “the dream”)?
- “We want to automatically [ what? ] so that [ what? ]”
- Can you deﬁne success?
- Is there a smaller step?
1. “WE WANT TO AUTOMATICALLY…”
- …this is your theory.
- (your experience plays a big role in ﬁnding a good bet)
“I wonder if we could [ solve the problem ] by using [ data in reach ]?”
2. “I RECKON….”
This is where you need some AI / machine learning experience.
- Literature search (has anyone already done this?)
- What would make a good model for this problem?
- What data do we have? (are we allowed to use it?)
- Would this model disadvantage any group?
- What data do would we need?
…and so, does this sound like something worth trying?
3. “WHAT IF WE TRY….”
- Building a stand-alone experiment (running code)
- Getting hands on: can we really do this? what gaps do we have?
- Does the model work, perform well?
- What conﬁdence do we have in this?
- How much data do we need to get to production?
- What are the consequences of the model being wrong?
4. “LET’S SEE IF OUR HUNCH IS RIGHT…”
- Run and monitor without using the results
- There’s usually lots of plumbing work to do
- Find out what is working and what is not
- Feedback behaviour to improve the model
- …and be sure you can rectify errors
5. “LET’S GET IT OUT AND SEE…”
IT’S MORE INCREMENTAL THAN THAT