Data is everything. The right inputs are the difference between a model in production or stuck going nowhere in R&D. ML teams are spending an average of 40% of their time wrangling it.

But how do you know if the dataset you have is right for the job? How do you understand the shape of your data? What about its impact on model performance or out in the real world?

We’ll focus on how to put data at the heart of your machine learning development process.

In this session, you will learn more about:

• How to bring your models to your data (instead of vice versa)
• What the common dataset management challenges are (and how to get around them!)
• Annotation process management in the context of your data
• How data visualization is critical to dataset and model development