Data is the key to unlocking AI software’s potential. But this doesn’t mean piling on more data. It means adding the right data and treating it with the same level of respect as we treat our code so we can curate datasets that allow our models to learn the correct information about the world. This is why data-centric development has become crucial in applying machine learning software.
But what does this mean practically? It means that iterating with our data needs to be reliable and easy. It means we need to free ourselves to curate our datasets to match the real world. And it means that we need tooling to help us do this.
In Developing Data-Centric AI Applications, we combine the labeling capabilities of Superb AI Suite with Pachyderm’s data management and automation platform. Together, these tools allow us to manage our data lifecycle and develop data-centered models for our applications.