Artificial intelligence sounds complex, but at its heart it is about how computers learn from data. Two of the most important learning styles are supervised and unsupervised learning. They are like two different ways of teaching: one with answer keys, one without. On Huawei Cloud, these ideas turn into real services that people can use for apps, reports, and smart products. As Skyloop, we help our customers understand these styles and choose the one that fits their needs and data.
Supervised learning is like studying with a workbook that has solutions in the back. Each example in the data comes with a correct answer, called a label. For instance, an image might be labeled “cat” or “car,” or a row of customer data might be labeled “will buy” or “will not buy.” The model learns to match inputs with labels by seeing many examples. On Huawei Cloud, ModelArts lets teams upload labeled data, run training jobs, and see how well their models perform. When labels are clear and the data is clean, supervised learning can give strong and reliable predictions.
Unsupervised learning is closer to exploring without a map. In this case, the data has no labels at all. Instead, the system looks for groups, patterns, or structures by itself. A common example is clustering, where the algorithm finds customers who behave similarly, even if no one has named those groups yet. Another example is reducing the number of features to see a clearer picture of the data. Huawei Cloud tools, such as clustering options in ModelArts and data exploration in DataArts Studio, help with this kind of early discovery. It is best to start with unsupervised methods when a customer has a lot of data but no clear labels or business rules yet.
The big difference between these two learning styles comes from the goal. Supervised learning is best when you already know what you want to predict. You have clear questions like “Will this transaction be risky?” or “What object is in this image?” and you have past examples to learn from. Unsupervised learning is more about asking “What is going on in this data?” It is useful when the business does not yet know which segments, patterns, or trends matter most. Therefore, when Skyloop designs a solution on Huawei Cloud, we first check what kind of answers the customer expects and what kind of data they actually have.
In summary, supervised learning uses labeled data to make direct predictions, while unsupervised learning uses unlabeled data to uncover hidden structure. Both are important and often work together in one project. On Huawei Cloud, they are not just theory but part of real platforms like ModelArts and DataArts Studio that Skyloop uses every day. In the next part of this series, we will look at concrete examples on Huawei Cloud and see how these two learning styles solve real problems in areas such as retail, finance, and public services.
