Super Data Science: ML & AI Podcast with Jon Krohn 580: Collecting Valuable Data
Jun 3, 2022
Dive into the world of machine learning as strategies for effective data collection take center stage. Discover how to identify commercial problems before starting your data journey. Learn the significance of labeled data and the various methods to obtain these crucial labels. The discussion also highlights challenges that may arise during the data collection process, setting the stage for future insights.
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Labeled Data Value
- Prioritize collecting labeled data over unlabeled data for commercial machine learning models.
- Labeled data, like images tagged as "dog" or "cat", enable training for specific, valuable predictions.
Automated Labeling
- Automate data labeling whenever possible, for example, by scraping existing labeled datasets.
- Inferring labels from star ratings on IMDB reviews demonstrates such automation.
Manual and Outsourced Labeling
- If automated labeling isn't feasible, consider manual labeling or outsourcing to labeling services.
- While more time-consuming and costly, this creates unique, proprietary datasets.