An engineer at heart and a builder by soul. Nir combines a unique background of public speaking, computer vision engineering, and MLOps research - to give a fascinating session on topics he lives and breaths.
Nir is leading the data science, advocacy, and outreach activity of DagsHub worldwide. He focuses his research on improving workflows for data science teams that work in a production-oriented environment.
Nir graduated with honors from the BGU structural engineering faculty, majored in Structural Analysis and Finite Element Simulations, and is currently pursuing his Master's in Data Science from Reichman University.
Jupyter Notebooks have seen enthusiastic adoption among the data science community to become the default environment for research.
But, are Jupyter Notebooks really the best home for data scientists to develop production-ready projects? The non-linear workflow, lack of versioning capabilities, no IDE integration, and inadequate debugging tools make it laborious to productionize a project created in a Jupyter Notebook environment.
Should we just throw our Jupyter Notebooks out the window and move to classic IDEs? Probably not – Jupyter Notebooks are, after all, a great tool that gives us superhuman abilities. We can, however, be more production-oriented when using them. How does this look in practice? That is exactly what we'll cover in this talk.