Data Selection for Data-Centric AI: Data Quality Over Quantity
Big data has been critical to many of the successes in ML, but it brings its own problems. Working with massive datasets is cumbersome and expensive, especially with unstructured data like images, videos, and speech. Careful data selection can mitigate the pains of big data by focusing computational and labeling resources on the most valuable examples. Cody Coleman, a recent Ph.D. from Stanford University and founding member of MLCommons, joins us to describe how a more data-centric approach that focuses on data quality rather than quantity can lower the AI/ML barrier. Instead of managing clusters of machines and setting up cumbersome labeling pipelines, you can spend more time tackling real problems.
In this episode
Cody Coleman
CS Ph.D., Stanford
Cody Coleman recently finished his PhD in CS at Stanford University, where he was advised by Professors Matei Zaharia and Peter Bailis. His research spans from performance benchmarking of hardware and software systems (i.e., DAWNBench and MLPerf) to computationally efficient methods for active learning and core-set selection. His work has been supported by the NSF GRFP, the Stanford DAWN Project, and the Open Phil AI Fellowship.
Demetrios Brinkmann
Host
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Vishnu Rachakonda
Host
Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.