The Importance of High-Quality Training Data for Building Machine Learning and Deep Learning Models

AI models are only as good as the data they’re trained on. Both machine learning and deep learning models are designed to make decisions based on past examples, but can lead to problems when they are not properly trained. 

If you want your AI model to be successful, it’s important that your data is clean and accurate. You need to ensure that your training data set has enough variety in order for the model to make the most accurate predictions possible. You also need to ensure that you have enough labelled data so that it can train effectively.Before we dive into the importance of high-quality training data and who can provide you with such data, let’s look at some definitions for better comprehension!

What is the difference between AI, Machine Learning, and Deep Learning?

AI, Machine Learning, and Deep Learning are all related fields in computer science. These terms are often used interchangeably, but it’s important to know the difference between the three:

Artificial Intelligence (AI) is an umbrella term that refers to any technology that can be described as “thinking” or “intelligent.” It refers to any attempt at building a computer system that mimics human behaviour. That can include things like facial recognition software or voice-to-text systems, but it also includes some complex systems that can learn from experience and make decisions based on those experiences.

Machine Learning (ML) is a subset of AI that focuses specifically on algorithms that can learn from data without being explicitly programmed by humans. ML uses algorithms (or sets of rules) so that computers can make decisions based on patterns they’ve observed in data sets. ML models automatically update their algorithms based on feedback from the user. 

Deep Learning (DL) is a subset of Machine Learning where many layers of neural networks are stacked on top of each other to create complex models with high accuracy. Neural networks are computer models that learn to solve problems based on examples and experience, without human intervention. Whereas machine learning models can be trained on smaller data sets, deep learning models require large amounts of data.

How can you build successful Machine Learning and Deep Learning models?

The answer is: high-quality training data. The accuracy of your machine learning or deep learning models is paramount to their success, and high-quality training data is the only way to increase the reliability of your models. Even if you can easily acquire the data you need, gathering it is only the first step. Most of the work lies within cleaning, labelling and classifying that data so it produces accurate results.

Here are three reasons why you need high-quality training data:

• AI bias or algorithmic bias refers to the tendency of machine learning systems to produce results that reflect the biases of their creators. It’s a growing issue as more and more companies adopt AI technology, which has the capacity to influence how we perceive the world around us. 
• Structural AI bias has ethical implications and occurs when the structures of algorithms or data sets are built to favour one group over another. This can happen in many ways, including when an algorithm is built to privilege existing power structures, or when it is built to prefer data from certain demographics over others.
• Statistical AI bias arises from improper data sampling or from mistakes made during the training process itself. Statistical AI bias is a problem that affects the conclusions drawn from data analysis. It occurs when data scientists use algorithms and models to make predictions about the future, but these predictions are not accurate because of flaws in the model. This can lead to unreliable forecasts, inaccurate risk assessments, and inconsistent decision-making processes.

Ensure realistic reflection of the market

The tech industry needs more diversity—diversity of thought, demographic backgrounds, and experience—to help prevent AI bias from creeping into their models. The good news is that there are ways you can help mitigate AI bias by doing things like sampling from diverse populations or using different types of data sets. It’s important to note that sometimes the bias that appears in AI is not intentional or expected, but it’s still harmful because a lot of people see AI models as neutral due to their “robotic nature”. Also, employing qualified data annotators from different backgrounds can help you eliminate stereotypes and prejudices.

Build practical AI models

The point of building and implementing machine learning or deep learning models is to automate processes so you can achieve greater efficiency and productivity while reducing costs and errors. If your model is based on data that hasn’t been sufficiently or properly trained and tested, then your AI model will generate inconsistent and incorrect outputs.

Learn how you can ensure the quality of your data sets from our article.