The greatest challenge when building a high-performance model isn't about choosing the right algorithm or doing hyperparameter tuning: it is about getting high quality labeled training data. Without good data, no algorithm, even the most sophisticated one, will deliver the results needed for real-life applications. And with most modern algorithms (such as Deep Learning models) requiring huge amounts of data to train, things aren't going to get better any time soon.
Active Learning is one of the possible solutions to this dilemma, but quite surprisingly, left out of most data science conferences and computer science curricula. By the end of this presentation, you will learn the importance of Active Learning and its application to make AI work in the real world.
Jennifer Prendki is the founder and CEO of Alectio. The company is the direct product of her beliefs that good models can only be built with good data, and that the brute force approach that consists in blindly using ever larger training sets is the reason why the barrier to entry into AI is so high. Recently, Jennifer was the VP of Machine Learning at Figure Eight, the company that pioneered data labeling, and she has been Chief Data Scientist at Atlassian and Senior Manager of Data Science in the Search team at Walmart Labs.
Jennifer has spent most of her career creating data-driven cultures, 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 holds a PhD in particle physics from Sorbonne University), 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 nontechnical audiences alike at conferences and seminars and is passionate about attracting more women to careers in STEM.