'ModelOps Introduction Series: Data Preparation In AI' by getmodzy modzy datapreparation
This blog focuses on the importance of Data Preparation in support of your AI projects and avoiding the common pitfalls. Data is the heart of every AI investment, but data is not a monolithic concept. It’s important to understand what it takes to gather and prepare these types of data to budget accordingly. No one should be doing AI for the sake of doing AI—it must be tied to a clear business objective. Data acquisition costs are often an overlooked contributor to an AI project.
Data is the heart of every AI investment, but data is not a monolithic concept. It comes in many different forms and sizes. For example, your data may be well structured and categorized, like the data you might find in a spreadsheet or database. Alternatively, your data could be an unstructured collection of text like newspaper articles or customer reviews.
The first consideration for good data preparation is the data provenance. There are a lot of good ideas for AI applications, with the caveat that the data can be acquired. Before green lighting any project, it is vital to have the team building the AI solution articulate the location of the data they are going to use so that you don’t waste money on other unnecessary expenses.
Asking these kinds of questions puts you in a position to determine whether or not the data is a good fit for the kinds of AI applications you are considering. Assuming that you are comfortable with the source and licensing constraints of the data, the next step is to understand costs. Some widely available datasets are freely available and licensed correctly for commercial use , but other data is available to a restricted members only or commercial audience .
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