They say that a lot of truth is said in jest. Let’s study the meme below to extract some valuable insights in Data Science and Machine Learning.
When studying this hilarious argument for why Napoleon and Celine Dion are extremely similar, we notice two errors that Data Teams all over make. Here they are-
Lesson 1: The impact of Cherry Picking
Given enough time, I can make the data sing however I want. By selectively picking angles and ideas, we might be able to draw conclusions that have beef with reality. However, most data teams do this unconsciously. Teams will spend hours tweaking every single parameter, only to never critically evaluate their data sources and collection methods. Often, ML projects fail not because of weak models, but because of a fundamental flaw in the underlying thought process/assumptions that no one caught.
Lesson 2: The Importance of Domain Knowledge-
How many Software/AI teams decide that they will start modeling their data without understanding the underlying domain/business statement. Zillow is the perfect example- they spent heaps of money on cutting-edge AI house price prediction, only to realize that their underlying business model was severely broken. Or take the meme below. If I blurred out the specifics, and just gave you the anonymized personality vectors- you’d probably instantly assume that the assertion that the data points were very similar. Too many data scientists just jump into modeling without taking the time to understand the dataset and the features. This is a huge no.
Remember- when it comes to Deployment Grade AI, you can probably take a lot of shortcuts in the AI Models. You can never compromise on your data processes.
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