How To Ensure Quality Of AI Training


Robotic Hand with Cylinder and Shape Sorting Toy. Machine Learning and Recognition Concept 3d Illustration

Ensuring the quality of artificial intelligence (AI) training comes down to data. As one of the essential elements of artificial intelligence, data is the starting point. It’s what makes AI applications run. Developing AI—for example, AI adopted by businesses—typically goes through three phases. The first is R&D (research and development), the second is training, and the third is implementation. The two first phases of AI development needs massive amounts of data. (1)

The other critical elements of AI, besides data, are processing or computing power and algorithms. These three are the fundamental components of AI development. Data’s role is why data labelling is crucial. It lets AI and ML (machine learning) algorithms accurately comprehend real-world conditions and environments.

Ensuring the quality of AI training

Training an AI consists of teaching the machine how to interpret data and learn from it. Like any other training, training an AI takes time to get the machine to perform a task with acceptable accuracy. By ensuring that an AI is trained to understand the data and make reliable predictions correctly, you can have an AI that performs optimally. AI will predict trends and create insights using the information fed to it.

But how can you ensure AI’s training quality? Below are a few guidelines.

1. Use quality data for a quality result

Your data should be comprehensive, clean, and useful. Not enough volume would make your AI unreliable for large-scale business applications. Keep in mind that no matter how complex and sophisticated, AI algorithms won’t be able to overcome a poor dataset. Limited but high-quality data can be helpful, but bad data can stop your AI’s progress in its tracks. Moreover, bad data can distort your AI’s judgments. You’ll also run the risk of creating biased AI.

An AI makes predictions based on data containing previous instances of the information you’re trying to obtain. So, make sure your historical data is accurate because this data is critical for making reliable predictions. An AI can predict accurately and give valuable insights with high-quality, factual historical data. Making sure you get quality data for your AI during training is vital for its accuracy and adaptability. (3)

2. Design a test set

For your AI to deliver accurate, useful results, avoiding mistakes like ‘overfitting’ during training is crucial. Overfitting happens when an AI predictive model performs too well on the data to which it was trained. The model becomes too narrow and specific to this data set that it becomes useless when used with data sets that contain new variables. If a model becomes ‘overfitted,’ it can no longer be applied to other data sets.

An overfitted model can be problematic, as predictive models should always have an unknown or unexpected capacity. And this unknown capacity needs to be subjected to tests and not just examined from a programming point of view.

To avoid ‘overfitting,’ a test set should be designed for AI during training, which could check for issues and validate AI’s algorithm. This process is typically automated and not used in training the algorithm—it’s simply another element of the training set that’s set aside or ‘sequestered.’

Once the algorithm is complete, the test set can be used to see how well the algorithm is trained. The test set would then use new data sets other than the ones used during training, which can ensure that overfitting and other training errors are avoided. (4)

3. Find out how much training data is needed

There is no one-size-fits-all when it comes to how much data an AI needs during training. Ultimately, the amount of data needed during training will depend on several factors, like the type of model you’re trying to build. The degree of complexity will also have to be considered, and the level of performance you want to achieve.

AI engineers typically try to achieve the most results using the minimum amount of data. This approach usually means that the engineers will first try simple models with few data points. After which, the engineers would then try to implement advanced methods that could require larger amounts of data. Generally, the more complex the problem, the larger the amount of data you’ll need for training. You’d also have to consider training methods, labeling needs, error tolerance, and input diversity.

4. Don’t discount human input

Including human judgment can help AI be more accurate. A model that’s too confident in certain classes can benefit from human guidance. Keeping humans in the loop doesn’t just mean labeling a few data points. Humans can help fine-tune an algorithm, like correcting machine errors in image recognition.

Accuracy in AI algorithms is a continuing effort; it requires training set validations and maintaining a level of human involvement. Today’s market is dynamic, and this constant change happens all around us. An organization that relies on AI for insights and trend predictions needs a system to keep up with the changes.


Ensuring the quality of AI training starts with data. Data, after all, is the backbone on which all the other elements of AI development rely. Ensuring you have quality data is a vital step during AI training. Designing a test set for AI algorithms is also crucial. The test can help your machine maintain the integrity of its results. Lastly, no matter how automated everything seems, maintaining a level of human involvement is critical for ensuring your system will remain as accurate as possible.


  1. “Why AI Would Be Nothing Without Big Data”, Source:
  2. “Three Basic Elements of the Upcoming AI Era”, Source:
  3. “How to Ensure Data Quality for AI”, Source:
  4. “Overfitting in Machine Learning: What It Is and How to Prevent It”, Source:
  5. “What is human-in-the-loop machine learning?”, Source:


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