This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps.
Above all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it's great that there are lots of smart people thinking about this really hard problem, and even though it is by no means 'solved' conversations like this make me feel cautiously optimistic about the space.
- Infrastructure for ML and Data Science | Outerbounds
- Metaflow Resources for Engineers | Outerbounds
- Metaflow Resources for Data Science | Outerbounds
- nbdev+Quarto: A new secret weapon for productivity · fast.ai
- nbdev – Create delightful software with Jupyter Notebooks
- Welcome to Metaflow | Metaflow Docs