Data-Centric AI: Best Practices, Responsible AI, and More
Data-Centric AI: Best Practices, Responsible AI, and More
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 50m | 321 MB
Instructor: Aishwarya Srinivasan
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 50m | 321 MB
Instructor: Aishwarya Srinivasan
Machine learning typically focuses on producing effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. Data-centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline.
In this course, Aishwarya Srinivasan covers the data-centric principles that guide our path forward in this new age of AI as we shift from a model-centric approach to a data-centric paradigm. Learn about DCAI—what it is and the value it offers. Aishwarya covers the DCAI workflow; MLOps as part of DCAI; data validation and preprocessing; model validation; bias detection and mitigation; responsible AI; and more.
Data-Centric AI: Best Practices, Responsible AI, and More
