Building a better AI data engine: How designing a workflow involving automation and iteration can lead to more accurate models, faster implementation and 70% cost savings.
AI is supposed to transform your business but enterprises are finding that it is taking much longer than anticipated to get to full impact. Data labeling and data management are at the heart of the problem. The work of creating and managing training data does not stop after an initial model is trained and deployed. To accelerate development, learn how leading AI teams adopt not just a data-centric approach, but the best teams are prioritizing the right data at the right stage of their project’s life cycle in order to iterate on these models quickly. This approach has been reinforced by new tools such as model assisted labeling and model diagnostic which Manu will dive into. He will also discuss how AI teams are using a training data platform to enhance their overall labeling operations.