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. Join us for this latest Data Science Central webinar where we’ll cover:
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 obtain those labels
How to tell if active learning is appropriate for your ML project
Speaker:
Jennifer Prendki, VP, Machine Learning – Figure Eight
Hosted by:
Bill Vorhies, Editorial Director – Data Science Central
