Data-centric AI is a rapidly growing, data-first approach to building AI systems using high-quality data from the start and continually enhancing the dataset to improve the model's performance. Data-centric AI is a modern approach to building AI where model accuracy is primarily dependent on data quality.
Data labeling may seem simple, but it isn’t always easy to implement at scale. And getting it wrong will delay your entire model training process. Learn how to develop your labeling strategy for scale and accuracy.
Zhuoru Lin, Data Scientist at Bombora, the leader in B2B intent data and Heartex customer, sat down with us to discuss how Bombora uses Heartex Label Studio to test and validate new NLP models.
To be fully effective, data scientists need to work with other roles as part of a team. As companies fully embrace data and build their data science departments, it is essential to establish the right processes and workflows first before proceeding to hire people with the right skills needed to implement these processes. Here are some important roles to consider when structuring a data science team.
Data labeling is a team sport. Data scientists, subject matter experts, engineers, operations, and annotators must work collaboratively to ensure quality results and an efficient process.Schedule Product Demo