The model wants what it wants: Strategies for label collection by Marina Angelovska

Collecting labeled data is one of the biggest challenges in machine learning. With millions of unlabeled instances available, the key is selecting the most valuable samples for annotation to maximize model performance. Marina Angelovska explores Active Learning as a strategy to optimize the labeling process.

By strategically choosing the most informative data points, Active Learning reduces annotation costs while improving model accuracy.

Watch this session to learn how to make the most of your data and enhance your ML pipeline efficiently.

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Видео The model wants what it wants: Strategies for label collection by Marina Angelovska канала Data Makers Fest