What Is AI Training Data?
AI training data is the fuel that powers AI. It is the raw material, or inputs, that provide machine learning algorithms with information about so-called domain knowledge. This input is used to teach machines how to learn what differentiation between meaningful and meaningless data points looks like in a given context. Given this background knowledge, an AI system can then take new observations and determine whether they are useful or meaningless on its own without human intervention.
The importance of AI Training Data in AI projects
AI enables organizations to automate human tasks that are repetitive, time-consuming, or both. These include processes that require logical reasoning, problem-solving, and decision making. Today's enterprise AI systems can achieve superhuman performance on these types of tasks.
Artificial intelligence is also being used to create new products, business models, and services that deliver more value to their customers. For this, AI training data is a fundamental input to a modern AI project. On the other hand, without access to AI training data, it becomes difficult to make decisions for any AI systems.
The more high-quality AI Training Data, the better
In fact, every step of the AI workflow requires good quality data. The more high-quality data you have, the more accurate your results will be and the more confidence you can have in taking them into production. Conversely, if your machine learning algorithms aren't getting the data they need, they won't learn correctly.
Without quality AI training data , it's almost impossible for an organization to deploy successful, high-performing AI systems. This is precisely why many organizations are now focused on improving the quality of their training data with insights derived from specific industry verticals.
How to get high-quality AI training data?
If you are working on a AI-enabled project, you are looking for high-quality data that is up-to-date. You want training data that offers a large number of examples so there are enough examples to give your AI the ability to learn. However, data from the public domain doesn't provide a viable option because it isn't enough and it contains duplicate information from other sources. Additionally, much of it is not categorized correctly, some of it is not up to date. Even academic research groups have been struggling with what they can find in the public domain.
Collecting AI training data on your own can be noisy and costly, which is why it’s essential to design data collection workflows to capture high-quality data. Without proper data collection methods from the beginning, the rest of your data pipeline concerns will be a moot point.
To avoid losing one of your most valuable assets, work with a data collection services partner that understands rules, regulations, and implications of data collection, while leveraging technology to enable you to develop machine learning at scale.
Kotwel is an emerging data service provider in Vietnam, offering high-quality AI training data for machine learning and AI. It provides data services such as data collection, data annotation and data validation that help get more out of your algorithms by generating, labeling and validating unique and high-quality training data, specifically tailored to your needs.