Improve your models with high quality training data
Properly labeled data is the foundation for successful machine learning projects and usually the slowest part of deploying AI models. Dataset preparation, including collecting, labeling and reviewing data is a tedious and time-consuming activity. Beyond labeling, teams have to deal with multiple tools to first train their models, and then deploy them for production scale.
Join Clarifai’s Michael Tolbert, Data Strategist, Trey Pierce, Solutions Engineer and Jeff Toffoli, Senior Technical Manager for a conversation, demonstration, and dialog on how to build scalable labeling operations that drive enterprise accountability, productivity, and profitability.
Learn about:
When to label internally and when to outsource
How to leverage automation into the labeling process
How to integrate your data labeling with production AI models
How to manage distributed labeling teams
