(T) Stanford University Computer Science department held last month its first SysML conference which: “is a new conference targeting research at the intersection of systems and machine learning. The conference aims to elicit new connections amongst these fields, including identifying best practices and design principles for learning systems, as well as developing novel learning methods and theory tailored to practical machine learning workflows.”
Areas of interests, but not limited to, include:
- Distributed and parallel learning algorithms
- Hardware-efficient model architectures
- Efficient model inference / model serving
- Visualization of data, models, and predictions
- Fairness and interpretability for ML applications
- Privacy and security for ML applications
- Customized hardware
- ETL, data preparation, feature selection, feature extraction
- Testing, debugging, and monitoring of ML applications
- ML system interfaces
- ML programming models and abstractions
All the videos conference presentations are on YouTube SysML Conference. Following are four of them:
Professor Michael Jordan from UC Berkeley, Perspectives and Challenges of ML:
In his session, Professor Jordan providing some interesting thoughts about today’s ML state of the art, and the economics of ML:
Professor Bill Dally from Stanford University about hardware for deep learning:
Professor Dawn Song from UC Berkeley on security and privacy of ML systems. Without data integrity, ML systems cannot operate:
Jeffrey Dean, Google Brain Project Lead on System and Machine Learning Symbiosis:
Mr. Dean presented some interesting thoughts on integrated ML into the design of system software, computer systems, and computer networks:
Note: The picture above is from Stanford Rodin Sculpture Garden.
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