Data quality is clearly paramount for a successful AI project, but how difficult is it to achieve?
Data Labeling
Azavea's mission is to create advanced geospatial technology and research for civic and social impact. They interviewed a handful of leading data labeling firms, and studiously ...
Gartner predicts 85% of AI projects will fail. One of the leading reasons is low-quality data labeling. High-performing machine learning algorithms require high-quality data. ...
After a decade of data work, we’ve learned AI development requires a winning combination of people, technology, and processes. Don’t overlook one important step before beginning ...
Your choices about tooling and workforce will be important factors in your success as you design, test, validate, and deploy any ML model.
Crowdsourcing seems to offer a cheap option for training machine learning models, but it’s rarely as inexpensive as it seems. Here are some of the hidden costs of the crowd.
Cloud workforce solutions and integrations with leading data labeling platforms support higher quality training data for machine learning.
