Tech Term Decoded: Training Data

Definition

AI training data is a collection of information, or inputs, used to train AI models to give accurate predictions or decisions. For example, if a model is being taught to recognize images of dogs, its AI training dataset will be made up of pictures containing dogs, with each dog labelled 'dog'. This data is inputted into the AI model as learning inputs, eventually enabling it to recognize dogs accurately in other, previously unseen images [1].


Training data in AI

Training data in Machine Learning [2]

Origin

There are interesting cycles in the history of training data. In the 1990’s, before Machine Learning dominated AI, programmers hard-coded rules to improve the performance of their systems, based on the behavior of their models. When Machine Learning came to dominate almost 20 years later, we returned to similar Human-in-the-Loop systems, but with non-expert human annotators creating the training data based on model behavior [3].

Context and Usage

Training data is used in the field of AI and machine learning. Training data is fed into an ML model, where algorithms examine it to discover patterns. This allows the ML model to give more accurate predictions or classifications on future, similar data [4]. Many industries are leveraging AI training data, including healthcare, finance, manufacturing, retail, and transportation, to improve processes, enhance decision-making, and gain a competitive edge.

Why it Matters

The quality and quantity of a collection of training data is key to the accuracy and effectiveness of machine learning models. The more diverse and representative the data is, the better the model can generalize and perform on new, unseen data. Conversely, biased or incomplete training data can lead to incorrect or unfair predictions [5].

In Practice

A real-life case study of a company practicing training data in AI can be seen in the case of Zindi, an African data science platform that works with many Nigerian and African researchers and companies. Zindi provides AI training data through its competitions and challenges, offering datasets for various AI projects, and also offers courses and resources to help users learn and improve their skills in data science and AI. This approach allows African data scientists and researchers to create AI solutions tailored to local African challenges by using locally sourced, contextually relevant training data.

See Also 

Related Machine Learning Data Categories:

  • Semi-Structured Data: Data with some organizational structure but not fully formatted like databases
  • Structured Data: Information organized in tables with consistent fields and relationships 
  • Test Data: Separate dataset used to evaluate a model's performance on unseen examples
  • Unstructured Data: Data without predefined organization
  • Validation Data: Data for tuning hyperparameters 



Reference

  1. Jaen, N. (2024). How AI is trained: the critical role of AI training data.
  2. Utp. (n.d). Introduction To Machine Learning Dev Community.
  3. Monarch, R. (2019). A Brief History of Training Data.
  4. Bigelow, S., J. (2024). Explore the role of training data in AI and machine learning.
  5. Transcribeme. (2023). What is AI Training Data & Why Is It Important?

Kelechi Egegbara

Kelechi Egegbara is a Computer Science lecturer with over 12 years of experience, an award winning Academic Adviser, Member of Computer Professionals of Nigeria and the founder of Kelegan.com. With a background in tech education, he has dedicated the later years of his career to making technology education accessible to everyone by publishing papers that explores how emerging technologies transform various sectors like education, healthcare, economy, agriculture, governance, environment, photography, etc. Beyond tech, he is passionate about documentaries, sports, and storytelling - interests that help him create engaging technical content. You can connect with him at kegegbara@fpno.edu.ng to explore the exciting world of technology together.

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