I recently caught up with David Willingham, Principal Product Manager, MathWorks to discuss the evolution of data-centric AI and how engineers can best navigate – and benefit from – the transition to data-focused models within deep learning environments. As research into data-centric AI continues, we can expect best practice to evolve to suit a growing list of applications. Greater levels of data optimization and collaboration between multi-disciplinary teams is also likely to follow in the near future.
Interview: David Willingham, Principal Product Manager, MathWorks
Snorkel AI Accelerates Foundation Model Adoption with Data-centric AI
Snorkel AI, the data-centric AI platform company, today introduced Data-centric Foundation Model Development for enterprises to unlock complex, performance-critical use cases with GPT-3, RoBERTa, T5, and other foundation models. With this launch, enterprise data science and machine learning teams can overcome adaptation and deployment challenges by creating large, domain-specific datasets to fine-tune foundation models and using them to build smaller, specialized models deployable within governance and cost constraints.