Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio

Get the slides: https://www.datacouncil.ai/talks/acti... ABOUT THE TALK: Today, with always more data at their fingertips, Machine Learning experts seem to have no shortage of opportunities to create always better models. Over and over again, research has proven that both the volume and quality of the training data is what differentiates good models from the highest performing ones. But with an ever-increasing volume of data, and with the constant rise of data-greedy algorithms such as Deep Neural Networks, it is becoming challenging for data scientists to get the volume of labels they need at the speed they need, regardless of their budgetary and time constraints. To address this “Big Data labeling crisis”, most data labeling companies offer solutions based on semi-automation, where a machine learning algorithm predicts labels before this labeled data is sent to an annotator so that he/she can review the results and validate their accuracy. There is a radically different approach to this problem which focuses on labeling “smarter” rather than labeling faster. Instead of labeling all of the data, it is usually possible to reach the same model accuracy by labeling just a fraction of the data, as long as the most informational rows are labeled. Active Learning allows data scientists to train their models and to build and label training sets simultaneously in order to guarantee the best results with the minimum number of labels. ABOUT THE SPEAKER: 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 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 non-technical audiences at conferences and seminars, and is passionate about attracting more women to careers in STEM. ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers. Make sure to subscribe to our channel for more videos, including DC_THURS, our series of live online interviews with leading data professionals from top open source projects and startups. FOLLOW DATA COUNCIL: Twitter:   / datacouncilai   LinkedIn:   / datacouncil-ai   Facebook:   / datacouncilai   Eventbrite: https://www.eventbrite.com/o/data-cou... - 🎟️ GET YOUR TICKET TO AI COUNCIL 2026 🎟️ Meet the world's top AI infrastructure minds where architects of AI share what works. Three days of high-quality technical talks and meaningful interactions. → https://aicouncil.com/sf-2026 ⚡ FIND US: X: https://x.com/AICouncilConf LinkedIn:   / aicouncilconf   Website: https://aicouncil.com/