Machine Learning Pipelines
Jordan emphasizes the importance of reproducibility in model creation and the need for ML engineers to easily package, certify, and deploy models in various environments. The discussion delves into the challenges faced by data scientists in reusing featurizers and modules within organizations, highlighting the necessity for an open format for collaborative model development.In this clip
From this podcast
Machine Learning Street Talk (MLST)
Jordan Edwards: ML Engineering and DevOps on AzureML
Related Questions