By Bernadette Wilson, on Invalid date

Types of Data Labeling Jobs

Businesses exploring the benefits of artificial intelligence (AI) and machine learning (ML) are discovering that they need additional resources to make their models work. Supervised AI and ML learning requires training data sets that teach models to recognize specific types of data and produce outputs. With the growth of AI and ML projects, the number of data labeling jobs is growing. Market research firm Cognilytica forecasts that the market for AI and ML data preparation will grow from $1.5 billion in 2019 to $3.5 billion by the end of 2024.

If you’re considering responding to job postings from businesses, data labeling companies, or even firms that give people the option of performing data labeling jobs online, you need to be aware that just as data can take many forms, so can data labeling jobs.

What Type of Work Are You Best Suited For?

Data labeling work can vary depending on what the AI or ML model is designed to do:

Data Labeling For Image Recognition

Computer vision systems can “see,” but they don’t recognize images without training. Data labelers help computer models to home in on specific images and recognize them. Data labelers use a platform that allows them to draw bounding boxes around specific images and label them in a way that the model can understand.

Data labelers can label video data as well as still images, but this may also require tracking the object moves throughout the video.

Labeling data for image recognition takes skill and attention to detail. For example, the data labeler must draw the bounding box around only the part of the image that the model that has the characteristics described in the label, whether that’s “tree,” “bicycle,” or “cat.” Including too much or too little of the image could result in inaccurate outputs. Data labelers also must sustain focus and work consistently.

Data Labeling For Voice Systems

Virtual assistants need to be ready to respond to any spoken question or request, and AI and ML models used to train these models require large data sets. Although there are data sets available to train models in basic speech recognition, a business will need to train its own virtual assistants or chatbots to understand voice communication that pertains to their specific industry. This can be a challenging task; the training data needs to teach the model to understand intent as well as literal words so the virtual assistant will provide a relevant response.

Annotating training data for speech recognition requires creating audio recordings, transcribing them, analyzing the recordings and checking their quality, then transferring data to the model.

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Data Labeling For Text Projects

Texting, emailing, and writing in Word are so familiar to people, but computer applications don’t understand unstructured text data. Training data sets for ML projects aimed at interpreting written language, for example, an automated messaging chatbot on a business website. Others may teach a computer vision system in a manufacturing plant to interpret information labels or teach a document management model, and need to train models to understand that different words can have similar meanings.

People who work in this area of data preparation may label images that contain text. They may also work with documents, identifying words or phrases, for example, that convey a writer’s sentiment in feedback about a brand’s product or service or enable a search for documents containing information on a common topic.

In addition to actually labeling data, data labelers may develop and use other skills that can help them produce training data sets for AI. These tasks may involve ensuring data quality and testing data sets to ensure they meet real-world standards for accuracy.

A Data Labeler by Any Other Name

When you’re searching for data labeling jobs, you’ll probably notice that hiring managers at two different firms may refer to similar positions in different ways. Data labelers are sometimes called data annotators since the formal term for labeling data is annotation. Data labeling jobs are also listed under categories such as “data associate,” “data specialist,” or “machine learning labeler.” Although positions such as data scientists, machine learning engineers, and computer vision engineers require specific degrees and experience, most data labeling jobs do not require a degree or previous experience. Some companies even offer the ability to perform data labeling jobs online, allowing you to work from home.

Data labeling jobs aren’t for everyone — they require the ability to focus for long periods, work consistently paying attention to the finest details, and spending the workday using a computer platform rather than interacting with people. But, for some people looking for an in-demand job that will ultimately help businesses and organizations around the world operate more efficiently and productively, a data labeling job may be the perfect fit.

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