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Budgeting For Your AI Training Data: Consider These 3 Factors


Before you even plan to procure the data, one of the most important considerations in determining how much you should spend on your AI training data. In this article, we will give you insights to develop an effective budget for AI training data.



Shaip AI

AI modules can only be as effective as their training data, and collecting the right set of data is a mammoth task. Before you even plan to procure the data, one of the most important considerations in determining how much you should spend on your AI training data.

In this article, we will give you insights to develop an effective budget for AI training data.
 

How Much Data Do You Need?

 
The volume of data you need directly influences the price you would end up paying. According to Dimensional Research companies, on average need close to 100,000 data samples for the effective functioning of their AI models.

With that said, the quality of the data you feed into your systems also matters; as poor-quality datasets, data bias, lack of relevant data, lack of annotated data could cost you time, money, and efforts.

Besides, how much data you need also depends on the use cases you define for your models which further will give you clarity on whether you need image, text, speech, or audio data.

There is no set formula or rule of thumb to calculate the price of AI training data or the quantity of it because the requirements are very unique and no two businesses can have the same AI training data budget.
 

The Price Of Data

 
To give you an idea of how datasets are priced, here’s a quick table.

Data Type Pricing Strategy
Image Priced per single image file
Video Priced per second, minute, an hour, or individual frame
Audio / Speech Priced per second, a minute, or hour
Text Priced per word or sentence


Again, this is just the pricing strategy. The actual pricing of datasets will completely depend on

  • The geographical location from where datasets have to be sourced
  • The complexity of the use case
  • The volume of data you require to train your ML models
  • The immediacy of data requirements etc

 

Open-Source Vs Data Vendors: Which to choose?

 
While open-source portals and archives are great data sources, chances are also highly likely that the datasets present could be obsolete or irrelevant. Besides, data could also be unstructured with tons of crucial data cells missing.

Whereas, data vendors seem to look expensive at first, however, what you get is an impeccable quality of data that needs no supervision or audit. You don’t have to spend countless hours sourcing or labeling data but just focus on making your product more functional.

 

Wrapping Up

 
By now, you would have understood that the answer you are looking for is not straightforward. That’s why you need experts like Shaip to assist you with your AI Training Data requirements.


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