Overall Satisfaction with Jupyter Notebook
Almost every large-scale enterprise today has started to have in-house ML models & my organization is no different. We have been making use of this wonderful tool for Data visualization purposes, consuming python libraries to get some insights from the larger datasets from different data sources. Also, we use it to train our ML models to make predictions & then use that to further fine-tune the models efficiently.
- User-friendly UI.
- Easy to debug at each code line.
- Great support for Python Math libraries.
- Advanced data visualization capabilities.
- Notebook sharing feature.
- Intellisense not up to the mark.
- Limited collaboration scope.
- No IDE integration supported.
- Can become sluggish at times when datasets are huge.
- Data visualization capabilities.
- Easy to understand interface.
- Supports Multiple Python libraries.
- Flexible pricing options made it an east to adapt tool.
- Comes in very handy for our product team to make predictions.
- Support for different data from multiple sources made it an go to tool.
Well, so far Jupyter Notebook has been the better tool for me. It gives us more freedom & has more ability to train ML models & do the data visualization more efficiently. It's easier to operate & has a very simple-to-understand UI & with the support for taking data from multiple sources, it becomes even more powerful.
Do you think Jupyter Notebook delivers good value for the price?
Are you happy with Jupyter Notebook's feature set?
Did Jupyter Notebook live up to sales and marketing promises?
Did implementation of Jupyter Notebook go as expected?
Would you buy Jupyter Notebook again?
Jupyter Notebook is well suited for someone who's trying to learn python programming with the aim of training the ML models & get some really insightful analysis out of it. It really is a very user-friendly tool for any beginner as they can check the output on any of the written code without having to wait for the whole code to be finished.