Artificial Intelligence (AI) and Machine Learning (ML) are some of the world’s fastest-growing technologies that are being adopted by several industries worldwide. Many businesses are using AI and ML to automate a wide variety of processes and improve their efficiency. One of the things to understand about AI and ML models is that they need access to training data in order to learn. Also, the usefulness of these models is largely based on the quality, quantity, and diversity of data used to train them.
The booming popularity of AI and the rising inclination towards building more accurate AI models using high-quality training data fuel the demand for AI training datasets. The AI training dataset market was valued at USD 1,864.91 million in 2022 and is expected to grow to USD 12,993.78 million at a CAGR of 21.4% during the forecast period.
Exploring The Most Common AI Data Types
Here’s a look at the different types of data and how they’re used to train AI models:
Numeric data: As the name suggests, numeric data is data whose values are always in a number form. This data type includes real numbers, integers, and floats and can be collected from experiments, surveys, and other sources. Numeric data is usually used to train AI models to identify patterns and recognize objects.
Categorial data: Categorical data is a form of quantitative data divided into groups or categories. This data type comprises discrete values like classes, names, and labels. Categorical data has applications in several areas of AI, including machine learning, image recognition, and natural language processing.
Image data: This data type consists of pixel values representing an image. Image data is mainly used to train AI models for classification and object recognition operations. This form of data can be collected from several sources, including scanners, cameras, and satellite imagery.
Text data: This data type comprises words and sentences and is mainly used to train AI models for text classification and sentiment analysis tasks. Text data for AI can be collected from various sources, including articles, social media posts, emails, customer reviews, and speech transcripts.
The Rising Demand for AI Training Datasets
Advancements in data collection technology and the massive growth of AI applications are some of the key factors driving the demand for AI training datasets. Besides, open data initiatives and the development of cloud computing technology are fueling the AI training dataset market sales.
Open data initiatives have made vast amounts of high-quality data accessible to businesses, researchers, and the general public. With cloud computing, companies can store and process massive amounts of data easily and affordably. Both of these factors have resulted in the growth of AI datasets. Furthermore, the increasing use of AI in different sectors, such as insurance and healthcare, is anticipated to boost the demand for training datasets.
Asia Pacific Is Anticipated to Expanded Rapidly Over the Forecast Period
The rising adoption of emerging technologies and the expansion of the AI training dataset market key players in the area are fueling the region’s growth. Organizations in developing economies like India are adopting novel technologies at a higher rate. Also, several large companies like Microsoft are focusing on their expansion in the Asia Pacific region. As such, the region is expected to expand rapidly in the upcoming years.
In March 2021, leading AI research company OpenAI introduced several large-scale language models trained on huge datasets. Besides, the AI research firm has released numerous open-source datasets that can be used to train various natural language processing models.
In June 2021, American multinational technology company Amazon introduced several datasets for training AI models. These datasets, which include the Amazon Web Services (AWS) Public Datasets, can be used by various industries.
AI training datasets are crucial for the development and deployment of AI models. The rising need among businesses for data-driven decision-making to stay competitive drives the AI training dataset market growth.