One of the toughest challenges for data
scientists, and big data engineers is gathering, preparing, and transforming
data, and creating data pipelines.
Gathering data for a machine learning or
big data initiative can be hard work. The mere act of getting it all together
can leave data teams so excited that they can overlook critical safeguards. The
result? You could have data that’s skewed, or biased, or data sets that are too
small to generate accurate models.
This is why it’s important to have a pre-ML
data checklist. A pre-machine-learning data checklist will ensure you have the
right data sets for your models, thereby improving your chances of success.
Here are some other benefits:
waste less time: A checklist allows you to save the
time you would normally spend trying to work through your own mental checklist.
errors: A pre-ML data checklist ensures you have
don’t overlook obvious mistakes, and have relevant, unbiased, and
cognitive load: By using a checklist, you remove
the burden of unnecessary cognitive load. This enables you to free up the brain
power required for more productive tasks such as model selection and tuning.