AI can only be as good as the data it’s trained with. Even the best machine learning (ML) algorithms will fail to perform if the training data is poor. The accuracy and efficiency of your AI and ML models start with the preparation of your AI training data.
AI or machine learning training data is the foundational component in building AI and ML models. This training data is fed into the system to teach machines the basic knowledge of their functions. Similar to humans, AI training data are like the lessons and experiences that we encounter to learn, process information, and make decisions. AI and ML models work like the human brain and process these AI training datasets so that they can learn, perform tasks, and make predictions on their own.
Since machine learning training data is crucial to how well ML predictions and performance work, good quality data should be fed with human supervision from the beginning to the end of the training process. If the algorithm is trained well, it will successfully recognize features, establish data connections, and develop confidence in its predictions or performance.
Related: Human-in-the-Loop Machine Learning: How Humans Keep AI Models in Check
These datasets can be in a variety of formats, such as images, videos, text, and speech, depending on the needs of the machine learning model. For example, computer vision models for autonomous vehicles and parking systems require high-quality AI training data in the form of images and videos of vehicles, pedestrians, and street signs, among others, which is further annotated to train the model.
However, your training data isn’t fixed. Real-world data changes over time and there is a need to constantly update your model to keep up with these changes. Most of the time, human annotation is needed to get well-prepared, good-quality machine learning training data.
Related: Continuous Machine Learning: Why is it important?
For machine learning algorithms to become more accurate, they may need different datasets during the application process. These data sets are categorized as:
Training data is the main data that is initially fed into the machine learning system to learn and understand its purpose. This data is the starting point of the program’s expanding knowledge base as more data is fed into the process.
For tracking model performance metrics, training and validation data are labeled, unlike testing data. Testing data is the final, real-world check of a dataset that has never been seen by the ML model to assess its efficiency and help provide an unbiased evaluation.
Following the training and testing set, the model is normally evaluated using a validation set. Validation data are subsequent data sets that are used by the machine learning model to validate the accuracy and improve the confidence of its results during the training phase.
ML models go through proper training to make accurate decisions. Below are the different approaches to training machine learning models.
Supervised learning is when labeled datasets are used to teach machine learning models how to classify data correctly in order to make accurate predictions. It is called “supervised” because the AI system is being instructed on what to look for through these tagged or sorted training data. Ensuring the quality and accuracy in the process of labeling the data requires upfront human intervention.
Unsupervised learning is an AI learning method where the program must analyze unlabeled datasets. The AI system is given only the input data and generates an output based on its own analysis and identified patterns. It’s called “unsupervised” since human intervention is not required in training.
Semi-supervised learning is a mix of supervised and unsupervised learning. In this method, the AI system is provided with small quantities of machine learning training data to begin with, and later on trained with large volumes of unlabeled data.In most cases, semi-supervised algorithms are the best choice for data analysis since they can handle huge amounts of data with only a small quantity of labeled data. They are faster and easier to implement in the ML models.
TaskUs has over a decade of experience providing data collection and AI services to the most disruptive brands worldwide. For instance, we leverage our expertise in data classification and ML to provide better, more efficient content moderation services.
One of the world’s biggest social media platforms partnered with Us to develop ML capabilities for text and image classification for safer user experiences. TaskUs was able to increase the quality score and productivity of the project by providing a critical human review/data classification initiative to identify gaps and potential improvements in our client’s ML model.