How to Get Started Working in Data Annotation
What is Data Annotation?
Data annotation is the process of labeling data to make it usable for machine learning models. AI models require massive amounts of data to train on, and for this data to be useful, it needs to be labeled accurately.
For example, in a self-driving car, annotators might label obstacles like pedestrians, animals, and other vehicles. There are many different types of data annotation tasks depending on the format of the data and what the model is trying to achieve. Some examples of include:
- Images: Labeling objects, people, or scenes in photographs or video frames. For instance, drawing bounding boxes around cars in street images for autonomous driving systems.
- Video: Tracking objects across multiple frames, annotating actions or events occurring in a video sequence.
- Audio: Transcribing speech, identifying speakers, or labeling sound events in audio recordings.
- Text: Categorizing documents, identifying sentiment, or marking named entities in written content.
Some people may have the impression that data annotation is low paying work, however there are many high paying tasks as well, especially those requiring expertise in a particular field. As investors continue to pour money into developing machine learning models, the need for experienced data annotators will continue for now.
What Skills and Experience Do I Need?
For most data annotation jobs, you will need to have good English comprehension and writing ability. Additionally, individual projects will often have their own specific requirements, such as a degree in the field you are applying for.
Where Do I Find Data Annotation Work?
There are various platforms offering data annotation work. Some of the most popular ones include Outlier AI, Data Annotation, and Remotasks.
The process for getting started typically involves:
- Signing up to the platform and providing your resume and identification, etc.
- Completing an initial screening test.
- If you pass, you will be allocated work periodically.
Because it can take a while to get approved after passing a test and you could stop receiving work from a given platform at any time, it’s advisable to sign up for multiple platforms to increase your chances of finding consistent work.
Cons of Data Annotation:
- The projects can have unclear or missing instructions.
- It can be hard to find consistent work.
- Some platforms can be slow to pay you.
For most people, data annotation will be best suited as periodic gig work rather than a stable full-time job.
Full List of Remote Data Annotation Platforms
Here’s a list of platforms where you can find data annotation work:
Outlier.ai: A platform where contributors help train AI models through tasks like prompt generation, evaluation, and improving AI reasoning, offering flexible, remote work opportunities.
DataAnnotation.tech: This platform connects users to data annotation and labeling jobs for AI training, specializing in text, image, and video annotation.
TELUS Digital: A global provider that offers remote data annotation tasks and services for improving AI systems through accurate labeling and model training.
Remotasks: A microtask platform where users can work on tasks like image annotation and transcription to train AI models, offering flexibility pand earning potential based on task completion.
Pareto AI: An AI-driven platform offering opportunities in data annotation, assisting in structuring and labeling datasets for machine learning models.
Your Personal AI: A specialized service where users contribute to AI development by annotating data, often personalized and customized according to the client’s requirements.
Twine: A freelance marketplace that connects professionals with AI-focused data annotation projects, including image labeling, transcription, and more.
Additionally, you can find data annotation work on these general freelancing and microtask platforms:
Upwork: A general freelancing platform where users can find various data annotation jobs, including text and image labeling for AI projects.
Amazon Mechanical Turk: A microtask site offering small data labeling and annotation tasks, providing flexible earning options based on task volume and complexity.
The Future of Data Annotation
Manual data annotation done by humans isn’t the only way to label data for machine learning. There are currently many data annotation tools that can automate the process using AI.
However, human annotators are still necessary for several reasons:
- Ensuring accuracy
- Performing certain tasks that AI struggles with
- Mitigating inherent biases in machine learning models
As natural language processing, image recognition, and other models continue to improve, data annotation will become increasingly automated.
Not only that, as more people are replaced by AI in white-collar jobs like data entry, many of them are likely to move to data annotation, increasing competition in the field and potentially reducing wages.
In the short and medium term, new tasks will emerge which don’t have existing models to perform automatic annotation. Therefore, the manual data annotation market is likely to grow for now.
However, the long-term stability of the field is unclear.
