The pillars of AI product development
1. Objective: seems obvious, but it's vital to document your objectives clearly so you can ensure everything you do afterwards is set up to drive towards this. Your objectives are going to be iterated on over time – as they should be – as you get more and more granular about the outcomes you want from your AI products.
2. Tracking & labelling: the saying goes "your models are only as good as your data", which is 💯. However, a vital subpoint here is that your data might be wasted if you lack a quality labelling strategy (i.e. what labels you use, how you collect them and ensuring they're not adding noise to your system).
3. Infrastructure: Investments here can help boost reliability, performance, efficiency and productivity. If your product is a vehicle, this is your engine.
4. Monitoring: the fact that AI models are black boxes makes this space so vital – when things break, you may not even realize that it has. And the impact can be disastrous. Easily one of the most underinvested spaces.
It's on top of this that you layer on your feature engineering and models itself. Invest in all four pillars to have compounding returns.