Hi lwhsiao, Raza here, author of the post. My high-level answer is weak-labelling overcomes cold starts and active learning helps with the last mile. More de

Hi lwhsiao, Raza here, author of the post. My high-level answer is weak-labelling overcomes cold starts and active learning helps with the last mile. More detail: We see weak learning as very complementary to active learning. By using labelling functions, you can quickly overcome the cold start problem and also better leverage external resources like knowledge bases. But most of the work in training ML systems often comes in getting the last few percentage points of performance. Going from good to good enough. This is where active learning can really shine because it guides you as to what data you really need to move model performance. At Humanloop, we've started with active learning tools but are also doing a lot of work on weak labelling.