*= Equal Contributors
Artificial Intelligence (AI) and Machine Learning (ML) have made tremendous progress in the recent decade and have become ubiquitous in almost all application domains. Many recent advancements in the ease-of-use of ML frameworks and the low-code model training automations have further reduced the threshold for ML model building. As ML algorithms and pre-trained models become commodities, curating the appropriate training datasets and model evaluations remain critical challenges. However, these tasks are labor-intensive and require ML practitioners to have bespoke data… *= Equal Contributors
Artificial Intelligence (AI) and Machine Learning (ML) have made tremendous progress in the recent decade and have become ubiquitous in almost all application domains. Many recent advancements in the ease-of-use of ML frameworks and the low-code model training automations have further reduced the threshold for ML model building. As ML algorithms and pre-trained models become commodities, curating the appropriate training datasets and model evaluations remain critical challenges. However, these tasks are labor-intensive and require ML practitioners to have bespoke data… Read More