Datasets are not collected and fed to the AI system straightforwardly.
Machine learning would be much faster if this were the case. Preparing data can be challenging, as AI developers need to make data suitable for machine learning.
This involves a number of key data preparation steps, as discussed below.
1. Problem Articulation
AI developers may have the expertise in building a machine learning system, but they may not have the best grasp of the problem their system is trying to solve.
So it helps to get the opinions of the system’s intended users. The insights of those who will benefit from it will be valuable.
Problem articulation, in a way, is like stepping back from the data to get a clearer and broader picture of what the data and AI system seeks to achieve.
2. Setting Data Collection Mechanisms
The next step is to determine how to collect and organize data. Here, the development team has to find out if data warehouses are appropriate for the purpose or if data lakes are more suitable. Decisions on handling data are usually the job of a data engineer.
However, there are instances when the developers have to decide on the data infrastructure to establish. This situation usually happens in smaller projects or during the initial stages of development.
3. Data Quality Checking, Annotation, And Formatting
Data annotation refers to the sorting and labeling of data. This step makes AI training data meaningful and usable for specific use cases.
This step is usually human-driven, but nowadays, there are automated and hybrid solutions that make data annotation significantly faster and more efficient.
On the other hand, data formatting is about converting data sets into a file format compatible with the machine learning system. It ensures data consistency and prevents unnecessary variables that may confuse the system.
Before the annotation and formatting, though, there is a presumption that the development team has already conducted data quality checking.
It is important to ensure that the data is reliable, not imbalanced or skewed to a specific outcome, and enough to represent realities the AI system needs to learn properly. Of course, it is also crucial to eliminate errors or misrepresentations.
4. Data Reduction
Data completeness does not equate to accumulating all kinds of data. Often, developers build AI systems intended for specific tasks, so it is better to reduce data to focus on the most relevant situations.
It is possible to undertake data reduction through attribute sampling, record sampling, and aggregation.
5. Data Normalization or Scaling
Data normalization entails aligning data values to a common scale and value distribution. It is similar to the goal of data formatting, except that it focuses on establishing a suitable scale for the system.
In addition, data normalization seeks to eliminate the undue influence of large magnitude variables over others with smaller magnitudes.
Creating, cleaning, and structuring datasets are critical steps in AI development. They ensure that the data used for machine learning is adequate, compatible, targeted, and on the proper scale.
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