Streamline Your Data Labeling And Experimentation Process
If you are working with data, you can change the world. You can tap the power of massive data, apply machine learning, and transform it into disruptive products and predictive insights. But you’re up against a big challenge: the race to usable data. And you’re not alone. About 80% of AI project time is spent on data-related tasks like labeling. Validating a high-performance ML model requires massive datasets in training, and each experiment must be documented.
Streamlining your labeling and experimentation process would accelerate your training and your time to market. Join our webinar to hear from deep learning and data-labeling workforce experts about:
- Three critical requirements for your data labelers to optimize testing
- Ways to streamline storage and testing environments to accelerate experimentation by up to 20x
- The time and resource benefits of automated experiment tracking
Philip Tester Moderator
Philip is Director of Business Development at CloudFactory, where he creates partnerships to help solve data-production problems for AI innovators.
Yuval Greenfield is an engineer who leads Developer Relations at MissingLink, where he uses the MissingLink platform for deep learning research and creates tutorials, marketing content, and technical presentations.
Bill Heffelfinger is Principal Solutions Architect at CloudFactory, where he works with technical partners to build relationships, integrations, and joint solutions to support high-quality end-to-end data labeling operations.