In the previous issue, I wrote about what MLOps suffers from. Now that I come to think of it, I have realized that it is worth writing about one more thing that stands in our way towards MLOps. You know this thing very well. It’s Jupyter notebooks. In fairness to Jupyter notebooks, they have become the standard way of prototyping ML models all over the industry. Because the notebooks are interactive and support visual outputs, there is no better way of exploring data and sharing the results. Integration with lots of data science libraries made Jupyter the heart of the ecosystem. Jupyter notebooks have aesthetics and are simple enough to be used by anyone. This makes notebooks a perfect tool.
You can write files directly from cells within Jupyter. This makes having model.py, trainer.py, or other scripts needed for an MLOps pipeline a breeze.