A free virtual event to explore data-centric approaches that make AI practical.

Join us live to gain an in-depth understanding of the data-centric AI approaches and learn best practices from real-world implementations. Connect with fellow data scientists, machine learning engineers, and AI leaders from academia and industry with over 30 virtual sessions, hands-on virtual workshop, and in-person social.  

Sessions

August 3 - 4:  8:30AM - 2:30PM Pacific Time

Two days of keynotes, fireside chats, and lightning talks on the following topics. Agenda coming soon.

• Industry applications of data-centric AI
• Best practices to accelerate AI workflows
• Automated labeling, cleaning and augmentation
• Synthetic data generation
• No-code/Low-code AI development
• AutoML and ML frameworks
• NLP and conversational AI
• Monitoring and governance
• Trustworthy AI

Snorkel Flow workshop

August 2, 9AM - 11AM Pacific Time

A two hour hands-on workshop where experts from Snorkel introduce you to the Snorkel Flow platform and show you how to build AI applications rapidly using a data-centric workflow. To learn more go here. Select the workshop option during the registration process to apply for a spot. Space is limited.  

In-person social 

August 4, 5PM - 7PM Pacific Time

If you're in the San Francisco bay area, join us for an in-person social to connect with your peers in the data science and AI/ML community. Location details coming soon. Select the in-person social option during the registration process to join us. Space is limited.

Agenda

A wholistic representation towards integrated AI

Presenter: Xuedong Huang AZURE AI

Applying engineering practices to data-centric AI

Presenter:  Matei Zaharia, DATABRICKS 

Automating data quality remediation with AI

Presenters: Bryan Richardson + Jacamo Corbo, MCKINSEY & COMPANY

Cleanlab: Datasets that fix themselves

Presenter: Curtis Northcutt, MIT

Closer look at the role of data in achieving fairness in the job ecosystem

Presenter: Sakshi Jain, LINKEDIN

Data and manual annotation monitoring for training data management

Presenter: William Huang, CAPITAL ONE

Data selection for data-centric AI: data quality over quantity

Presenter:  Cody Coleman, CO-ACTIVE AI

Data-centric AI at comcast in voice and conversational interfaces
Presenter:  Jan Neumann, COMCAST

DataPerf: benchmarks for data-centric AI development

Presenter:  Vijay Janapa Reddi, HARVARD UNIVERSITY

HoloClean and Kamino: Structured Learning for Data Cleaning and Private Data Generation A

Presenter: Ihab Ilyas, APPLE

Leveraging synthetic data to train perception models using NVIDIA omniverse replicator

Presenter: Nyla Worker, NVIDIA

Machine Learning at Bloomberg

Presenter:  Gary Kazantsev, BLOOMBERG

MLCommons and Public Datasets

Presenter:  Peter Mattson, GOOGLE 

NASA spaceML worldview search: a NoCode earth & natural disaster dataset curator from unlabeled petabyte scale imagery

Presenter:  Anirudh Koul, PINTEREST

Petabyte-level learning

Presenter:  Peter Davio, BLACK SWAN DATA

Practical paths to data-centricity in applied AI

Presenter: Robert Crowe, GOOGLE 

Recommender systems at KeyBank

Presenter:  Ty Tinker, KEYBANK 

Repeatability, responsibility, & reliability – three Rs to make ML models more trustworthy

Presenter:  Sriram Subramanian, IDC

Reusable Data Cleaning Pipelines in Python

Presenter: Jason Goldfarb, STATEFARM

Scaling NLP to the next 1,000 languages

Presenter:  Sebastian Ruder, GOOGLE 

Toward Superhuman Communication Assistance

Presenter: Timo Mertens,  GRAMMARLY

Transforming drug discovery using digital biology

Presenter:  Daphne Koller, INSITRO

Unmasking Human Trafficking Risk in Commercial Sex Supply Chains with Machine Learning

Presenter: Hamsa Sridhar Bastani, WHARTON SCHOOL, UNIVERSITY OF PENNSYLVANIA 

....and more! 

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