We made an experimental foray into the ML space in attempts to accelerate authoring of SQL queries.
After about a week of reading through literature and playing with open ai, it became pretty obvious we were still super far away from being able to build something the business could actually find value in.
My problem scales horribly with the ML training angle we have today, because it's the super complex one-off queries we need the most help with, not the simple ones we can anticipate and train against.
What we need is actual intelligence for many of our problems. Things like subjective criteria are important to us. Realizing maybe a recursive query is a fair compromise to reduce a 400 line monster to 30 lines. Assuming the 400 "looks nasty", that is. I guess you could train that bit too, but then your solution space gets even more impossible to target.
After about a week of reading through literature and playing with open ai, it became pretty obvious we were still super far away from being able to build something the business could actually find value in.
My problem scales horribly with the ML training angle we have today, because it's the super complex one-off queries we need the most help with, not the simple ones we can anticipate and train against.
What we need is actual intelligence for many of our problems. Things like subjective criteria are important to us. Realizing maybe a recursive query is a fair compromise to reduce a 400 line monster to 30 lines. Assuming the 400 "looks nasty", that is. I guess you could train that bit too, but then your solution space gets even more impossible to target.