What is training data?

All of machine learning starts with a data set, or a collection of data. A dataset could be made up of spreadsheets, video footage, web pages, PDFs, or any other type of data. Generally speaking, the more training data that is fed into a model, the better the model’s performance. But it’s not just the quantity of data—the quality of the data is also highly important.

AI training data consists of features, also called attributes, which describe data. For example, a data set about a piece of factory equipment might include temperature, oscillation speed and time of last repair. This data is “fed” to a machine learning algorithm, a set of instructions expressed through a piece of code that processes an input of data in order to create an output. Feeding data to the algorithm means providing it with input data, which is then processed and analyzed to generate the output. A trained mathematical model is the result of this process. These models are the basis for nearly all recent innovation in artificial intelligence.

Some models are used for natural language processing (NLP), which can be used to teach machines to read and speak in human language. Computer vision enables them interpret visual information. But it all starts with training data.

Different types of learning algorithms use different approaches to training data. Supervised learning uses labeled data, while unsupervised learning uses unlabeled data. Semi-supervised learning combines both.

Supervised learning is a machine learning technique that uses labeled datasets to train AI models to identify the underlying patterns across data points. Labeled data includes features and labels, corresponding outputs which the model uses to understand the relationship between the two.

Many businesses hire large teams of human data annotators, which are sometimes assisted by machines. These annotators often require domain expertise in order to ensure that data is properly labeled. For example, when labelling legal data, annotators might need a background in law. The process of using human annotators to help ensure proper labelling is sometimes referred to as “human in the loop.”

A classic example of supervised learning is spam detection. To teach a model to identify spam, one could expose it to a dataset comprised of thousands of emails, each labeled by humans as either “spam” or “not spam.” The model would review the patterns in the emails, noticing various patterns. For example, emails that have the word “free” in the subject line are more likely to be spam. The model would calculate the statistical likelihood that the word “free” in the subject line corresponds to the label “spam.” Then, when given a new email with no label, the model can apply that calculation, along with many others, to determine whether the new email is spam or not.

This type of machine learning is called “supervised” because it involves human supervision to label all of that data.

Unsupervised learning models work on their own to discover the inherent structure of unlabeled data. Whereas supervised learning is helpful for mapping inputs to outputs, unsupervised learning is better suited to find patterns, structures and relationships within data itself, without any guidance on what to look for.

For example, imagine an advertiser wants to group customers into distinct segments based on purchasing behavior without knowing the categories in advance. An unlabeled set of data might include features like purchase frequency, average order value, types of products bought and time since last purchase, but it doesn’t have columns for “type of customer.” That’s what the model is trying to figure out. A clustering algorithm might be used to identify three clusters:

1. High-spending, frequent buyers

2. Occasional discount shoppers

3. New or one-time customers

The model learned the patterns on its own and made these groupings directly from the training dataset.

Data is all around us. The global population generates immense amounts of data every second of the day. But raw data is typically not useful for model training. Quality assurance is critical. First, data must be pre-processed through a multi-step data pipeline. This can be an involved process for data scientists, comprising a large portion of the scope of a machine learning project, requiring sophisticated data science tools and infrastructure. Poor quality data can introduce noise and bias, which prevents machine learning models from making accurate predictions, but high-quality training data allows models to produce more reliable results across innumerable use cases, from automation to to translation to data-driven decision-making

First data must be collected. For AI systems like autonomous vehicles or smart homes, data collection might happen using sensors or IoT devices. Government agencies, research institutions and businesses often provide public datasets. Advertisers use clickstreams, form submissions and behavioral data from users.

Raw data often contains missing values, duplicates and other errors. Once data is collected, it must be cleaned to correct these errors. This can be as straightforward as standardizing formats, like ensuring that dates appear as MM/DD/YYYY. After cleaning, data often needs to be transformed into a format that is easier for algorithms to process. Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.

To evaluate how well a model generalizes to new data, the dataset is typically divided into three sets. The first is a training set which is used to adjust a model’s parameters to find the best match between its predictions and the data, a training process called “fitting.” The second is a validation data set which is used to fine-tune hyperparameters and prevent overfitting. Finally a testing data set is used for final evaluation of model performance.

Sometimes called “human annotation,” data labelling is the process of adding meaningful labels to raw data so that a model can learn from it. Labels can describe any property of data. For example, a social media post saying “This product is terrible,” could be labeled as a “negative sentiment” in a process known as sentiment analysis. A human annotator could label a photo of a dog as “dog.” A bank transaction could be labeled as “fraudulent.”

Further steps may include data structuring, augmentation, and versioning. Some workflows include a feedback loop wherein analysis reveals where more or better data is needed, or where unuseful data can be filtered out.

Because data is just as important as model architecture, there is a lot of attention paid to optimizing the data training process. Synthetic data is one area of innovation. Instead of scraping huge real-world datasets, organizations are now generating synthetic data using AI itself.

Another trend is smaller, higher-quality datasets. Big models don’t just need more data, they need better data. Data scientists are building smaller datasets or task-specific datasets that are useful for narrow use cases. For example, an LLM used in the legal services field could be trained exclusively on legal corpora for better results.

The work of pre-processing data described in this article can be done automatically with AI. Newer algorithms help scrub hug datasets cleans, removing low-quality text, duplicate content and irrelevant boilerplate material, saving time and compute.

These are just a few recent trends in a quickly developing field.