Streamlining Model Deployment
Automating the transition from Jupyter notebooks to production environments can significantly reduce the stress and time for data scientists. The conversation highlights the common bottleneck where data scientists produce models faster than they can be deployed, emphasizing the need for effective engineering support. With solutions like Modelbit, the aim is to bridge this gap, potentially achieving a one-to-zero ratio of data scientists to engineers needed for deployment.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
699: The Modern Data Stack — with Harry Glaser
Related Questions
How does version control help in machine learning, as discussed in the episode 699: The Modern Data Stack — with Harry Glaser and the clip Version Control for Data?
How does version control help in machine learning, as discussed in episode 699: The Modern Data Stack — with Harry Glaser, and in the clip Version Control for Data?