There's a difference between training and inference (using the model to achieve a goal). Training is when you show examples (instances of something) to an enti

There's a difference between training and inference (using the model to achieve a goal). Training is when you show examples (instances of something) to an entity and it learns to recognize them. Example: doing homework and math exercises. Inference is when you show the entity an example it has not seen before and ask it to draw form its training experience to make something of that example. Example : a seasoned cop has had many more interactions than a green cop. However, sometimes People are more intuitive than others and dont need that many years to read situations. Their learning algorithm is different, or they're looking at things others are not looking at (features). You probably are using machine learning inference on your mobile device when you text and it recommends the next word. This application does not request a server because it needs to be low latency. You type fast, and you need the model to be right there. The same case is to be made for self driving. This poses several challenges and relies on several techniques to get the models on constrained environnements, either to run or get them there in the first place. Second, for training ? It depends on the problem you are solving. Are you trying to predict something that is so rare that even if you have a year worth of data, it only has happened twice? Is there a lag between influencing factors and the phenomenon ? Say, changing nutrients for a plant and its state not instantaneously changing. This depends on the problem.