Angler: Helping Machine Translation Practitioners Prioritize Model Improvements
*=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 that practitioners construct small targeted test sets to estimate an error’s nature, scope, and impact on users. We built on this insight in a case study with machine translation models, and developed Angler, an interactive visual analytics tool to help practitioners…
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…
2024 was the year machine learning (ML) and artificial intelligence (AI) went mainstream, affecting peoples' lives in ways they never before could have.
Machine learning (ML) practitioners using PyTorch tell us that it can be challenging to advance their ML project beyond experimentation. That's why over the last year, we've prioritized development workthat makes it easier for PyTorch users to deploy models in the cloud using Vertex AI. Vertex AI is a fully-managed…