Speaker "Jennifer Prendki" Details Back



One Smooth Stone: How to Beat Goliath with Quality Training Data


As Data Scientists, we have become accustomed to thinking that a better model accuracy implies injecting more data. Deep Learning models, for example, are traditionally known to be extremely data-greedy, but it seems that collecting a lot of data is the necessary price to pay to obtain a high-performance model. However, in the age of Big Data, this doesn’t seem like a major issue. The caveat though, is that Supervised Machine Learning algorithms require structured, labeled data, and the process of labeling data is unfortunately neither an easy nor a cheap task. Thankfully, there is a better way: Active Learning. In this talk, I will dig into the concepts of Semi-Supervised Learning and Active Learning before explaining how Figure Eight helps companies make AI work in the real world at a fraction of the labeling cost.


Jennifer Prendki is currently the VP of Machine Learning at FIgure Eight, the essential human-in-the-loop AI platform for data science and machine learning teams. She has spent most of her career creating a data-driven culture wherever she went, succeeding in sometimes highly skeptical environments. She is particularly skilled at building and scaling high-performance Machine Learning teams, and is known for enjoying a good challenge. Trained as a particle physicist, she likes to use her analytical mind not only when building complex models, but also as part of her leadership philosophy. She is pragmatic yet detail-oriented. Jennifer also takes great pleasure in addressing both technical and non-technical audiences at conferences and seminars, and is passionate about attracting more women to careers in STEM.