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Use GitHub, but not the web-interface GitHub, but the web-interface of the website is more useful for the purpose of this case study. To analyze categorical data such as word vectors it is very helpful to have a visualization tool like D3. Finally, running code for longer periods is slow as it is not designed to be used for this purpose.
9.
Some obvious problems with this approach include risk of a stall, time to recover and the need to plan ahead. It is even possible to do this offline. Figure 7.8. Data analysis in a local environment. Although the calculation takes longer and runs locally on the machine.
10.
The research outlined above therefore provides a basic justification for running data analysis using the cloud. 9
11.
For most business applications running on a personal machine, this is not an issue as the calculation will be done as the employee is using their personal machine.
12.
10 There is a relatively small difference in the cost between running on a personal machine and running on a Google Compute Engine instance. Therefore it might make sense to use a personal machine initially to try things out. A disadvantage of this approach is that it is difficult to get feedback. As we are not really “running” the analysis on the cloud, it will not show us any intermediate outputs. This can be a problem, for example, if you need to see where your program is “executing”.
13.
Kaggle has developed an enterprise-class data analysis platform. It has its own notebook interface, and support for both running training and running inference on data. This makes it really easy to do a quick test of a new idea and then run a larger production analysis on the cloud. I would recommend doing a quick test with the notebook interface using a “small” dataset. Then I would try the production environment using a larger dataset. This approach allows you to verify that your analysis is working in the cloud before you run it in production. Then use your imagination to visualize the resulting data.
14.
11 Define three data analysis use cases that you would like to run on the cloud, eg a word2vec model to get the similarity between words. 1. Create a notebook on Kaggle with the cloud service project 2. The project definition file tells Kaggle to provision a Compute Engine instance 3.
16.
They provide a dashboard interface on their site which is convenient
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