A machine learning model is only as good as the quality of the training data. However, creating the necessary training data is often time-consuming and expensive. Most of the models created today rely on humans manually labeling the data in a manner that allows your model to learn how to make the right decisions.
data labelling is the process of identifying and adding labels to raw data to specify the context for the Machine Learning models to make accurate predictions. Research from the analyst firm Cognilytica shows that approximately 80% of the time on AI projects is used to gather, organize, and label data. This is time that project teams can save and refocus on more strategic goals by using a data labeling platform. Outsourcing data labeling will free up skilled human resources to focus on more analytical and strategic work that will get business value from the data.
Approaches to data labelling
Data labeling is a critical step in developing high-performing Machine Learning models. Companies need to weigh various factors to use data labeling techniques and choose the best approach effectively. The common data labeling approaches are discussed at length below:
outsourcing
This is a popular approach to data labeling in which external labelers are hired through data labeling platforms. It is an excellent choice for temporary, high-level projects. Besides individual freelancers, companies can hire managed teams with ready-built labeling tools and previously vetted staff.
Internal data labelling
Companies can also choose to use internal data scientists who provide the highest quality labeling with greater accuracy. However, this approach is very time-consuming and is best suited to companies with substantial resources.
Programmatic labelling
This automated process has reduced the need for human annotation and takes a shorter time as it uses a script. However, HITL (Human-in-the-Loop) is still needed for quality assurance due to the possibility of technical problems.
synthetic labelling
Synthetic labeling generates new data from pre-existing data sets, improving time efficiency and data quality. Nevertheless, this approach needs immense processing power that drives up the price.
crowd sourced
This is a faster and more cost-effective approach to data labelling. It works by obtaining annotated data from several freelancers signed on to crowdfunding platforms. Nonetheless, the greatest downside is the variations in project management, staff quality, and data quality across several crowdfunding platforms.
Labeled vs. unlabeled data
Machine learning uses both labeled and unlabelled data. So, what are the main differences between them? First, labeled data usually has predefined rags such as type, number, or name, while unlabeled data possesses no names or tags. Second, labeled data has a wide range of uses and can be used in determining actionable insights, while unlabeled data has limited applications.
Labeled data is also more difficult to get and store (in relation to time and cost), while unlabeled data is easier to get and store.
Uses of data labelling
Data labeling can be used to increase the usability and accuracy of data in several contexts across various industries. However, it is most commonly used in the industries discussed below.
1. Audio processing
This is a technique where different types of sounds are converted into a structured format to allow its use in Machine Learning. These sounds could be animal noises and human speech, among others. You must first manually transcribe the sounds into written text, categorize the audio, and add tags to find more detailed information.
2. Computer vision
Computer Vision is a branch of AI that builds a computer vision system that derives useful information from visual input such as videos and images. This is done with training data that helps the computer locate key points in an image and discern the objects’ locations. This rapidly growing industry has uses in several industries, such as automotive, manufacturing, and energy.
3. Natural Language Processing
NLP tags essential text sections with certain labels to generate the training dataset. It has increasing uses in machine translation, spam detection, text summaries, virtual assistants, voice-operated GPS, and sentiment analysis.
Benefits of data labelling
Although the cost of data labeling is quite high, it is well worth the investment as a more accurate date usually improves the model’s predictions. Below are some of the benefits of data labelling:
- Precise predictions: Accurately labeled data gives a higher quality assurance with machine learning models, allowing them to learn and give the expected output. A model supplied with inaccurate or poor data will generate abrupt results.
- More usable data: Data labeling also improves the usability of data within the model. Data usability is a top priority when using data to build NLP and computer vision models.
- Lower human involvement: Accurately labeled training significantly reduced the need for human involvement and input. This generally reduces the associated costs of machine learning and AI-enabled technologies.
Data labeling is a critical part of data preprocessing for Machine Learning, and its effects and uses are far-reaching. The performance and effectiveness of AI-powered technology would reduce drastically if the data were inaccurately labeled. Every company in the AI and ML space should develop efficient strategies for data labeling if they are to harness and leverage the industry’s full potential!
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