The increased availability of computer resources and the prevalence of high-quality training data combined with smart learning schemas have resulted in a rise in successful AI deployments. However, many organizations simply have too much data, posing a challenge for data scientists: unless at least some of that data is labeled, it’s essentially useless for any ML approach that relies on supervised or semi-supervised learning. So, which data needs to be labeled? How much of a dataset needs to be labeled for an ML application to be viable? How can we solve the problem of having more data than we can reasonably analyze?
One promising answer is active learning. Active learning is unique in that it can both solve this data labeling crisis and train models to be more accurate with less data overall. Download this eBook to learn:
- The pros and cons of active learning as an approach
- The three major categories of active learning
- How your active learner should decide which rows need labeling
- How to tell if active learning is appropriate for your ML project