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.

image

Modzy Hacker Noon profile picture

@modzyModzy

A software platform for organizations and developers to responsibly deploy, monitor, and get value from AI – at scale.

Welcome to the first installment in our series on ModelOps. This blog focuses on the importance of Data Preparation in support of your AI projects and avoiding the common pitfalls.

image

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. While many other types of data exist, these two categories – structured and unstructured – power the most common AI applications being developed today. 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. Whether the AI application is aimed at improving an internal process, developing a new consumer product, or gaining a competitive advantage, all AI applications stem from mining data. For this reason, well-conditioned data is vital to the AI development process. As a budget owner, the first question you need to ask is “How much is data acquisition and preparation going to cost?”

A complete answer to this question will cover eight areas.

  1. Does the team know where to find relevant data?
  2. Is data already available to your organization? …POS data for instance?
  3. Is there a cost to acquire the data?
  4. Is the data labeled?
  5. Will the labeling be done in-house or by a third party?
  6. Where will the data be stored?
  7. How will the data be conditioned and formatted for the AI application?
  8. How will the data be delivered to the team building the AI application?

The first consideration for good data preparation is the data provenance. There are a lot of good ideas for AI applications,…

Continue reading: https://hackernoon.com/modelops-introduction-series-data-preparation-in-ai-tf2h32ff?source=rss

Source: hackernoon.com