Model Ensembling Techniques
Jordan discusses the concept of merging models trained on different datasets through ensembling techniques like knowledge distillation. He emphasizes the importance of optimizing model performance for real-time inference and the challenges in deploying models to clients efficiently.In this clip
From this podcast

Machine Learning Street Talk (MLST)
Jordan Edwards: ML Engineering and DevOps on AzureML
Related Questions