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Streamlining the Data Scientists Workflow
- Data visualization: No one spent too much time visualizing and exploring data. The human visual system is powerful for detecting patterns and identifying oddities, visualizing data leverages that. And they have found becoming really fluent in using it empowering.
- Outlier detection: Manually inspecting a small number of individual data points helps you get a feel for the typical structure of the data and oddities present within it. Leveraging outlier detection helps focus your attention on potentially concerning or enlightening oddities.
- Data pipelines: Your goal should be to have a single command that causes your end-to-end workflow to run, starting from the inputs you or your team is given and ending in the outputs. This removes a lot of room for errors that you can control by making the workflow explicitly defined and intermediate results inspect able and the code for the workflow should live in version control.
- Model inspection: Black box model inspection e.g. variable importance and partial plots can help gain confidence in a model or raise red flags and highlight input features that merit closer inspection. Many broken models are actually the result of data cleaning errors and oddities that weren't detected earlier in the process. White box model inspection e.g. coefficients of a small linear model, structure of a small decision tree, which input patterns, maximize an intermediate neuron in a neural network may be helpful here as well.
- Change detection: Detecting and alerting changes in input data distributions and/or model outputs can provide an early warning for input data issues that need to be cleaned up.