What are some ways to apply Create ML and Core ML to everyday tasks? What are the best tasks for Core ML models?
Really, the limit’s your imagination! But machine learning methods, generally, work well if you have:
- a well-defined objective (in other words, a clear, unambiguous criterion which tells you whether a classification is the right one or how far an estimate is from its true value)
- enough training data.
That’s true of a lot of different problems! Image, audio and text classification are all things which are applicable to a lot of real-world problems.
The key way to take advantage of ML is identifying problems where the goal can be clearly defined but the method is tricky. It’s not worth training a ML model to say whether something is red or blue – the average pixel value tells you that. But determining the class of an object (or reading text out of an image, or…) – these are problems where you can define what a success is unambiguously, but coming up with heuristics for the problem is harder. That’s where you’ll get most “bang for the buck” from ML methods!