Symphony: Composing Interactive Interfaces for Machine Learning
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a…
*=Authors contributed equally Machine learning (ML) models can fail in unexpected ways in the real world, but not all model failures are equal. With finite time and resources, ML practitioners are forced to prioritize their model debugging and improvement efforts. Through interviews with 13 ML practitioners at Apple, we found…
Google Cloud Next starts this week, and features over a dozen sessions dedicated to helping organizations innovate with machine learning (ML) and inject artificial intelligence (AI) into their workflows. Whether you’re a data scientist looking for cutting-edge ML tools, a developer aiming to more easily build AI-powered apps, or a…
2024 was the year machine learning (ML) and artificial intelligence (AI) went mainstream, affecting peoples' lives in ways they never before could have.