Item: Jupyter Notebook
Rating: 7
Author: Yaxian Xie

Use Cases and Deployment Scope

I use Jupyter Notebook to run statistical analysis and develop machine learning models and deploy in production. Jupyter Notebook is widely adopted by data scientists in our organization. It's well integrated with our remote cloud desktop as well as production system. We use Jupyter Notebook to understand customer behaviors and how to improve customer experience.

Pros and Cons

Big data analysis on cloud desktop.
Exploratory analysis.
Common machine learning models.

Nicer output format for explanatory analysis.
Easy update on packages.
Better compatibility with AWS tools.

Return on Investment

I think it's positive as it helps to deliver machine learning models.
It's free.
It's well integrated with AWS tools.

Alternatives Considered

Python IDLE

I selected Jupyter Notebook because this is better integrated with the existing production systems than optional tools (for example, R). It is also commonly used tool within the scientist community.

Key Insights

Do you think Jupyter Notebook delivers good value for the price?

Yes

Are you happy with Jupyter Notebook's feature set?

Yes

Did Jupyter Notebook live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of Jupyter Notebook go as expected?

Yes

Would you buy Jupyter Notebook again?

Yes

Other Software Used

Amazon SageMaker, Amazon Redshift, Amazon Athena

Likelihood to Recommend

I think Jupyter Notebook is well suited for scenarios below:
1) analyze big data above millions of records
2) develop machine learning codes that can be deployed in production system

I think Jupyter Notebook is less appropriate for scenarios below:
1) quick and easy statistical analysis
2) entry level users

Light user of Jupyter Notebook

Overall Satisfaction with Jupyter Notebook