Data Annotation and What Data Annotation Companies do
Data annotation is one of the core functions of machine learning. The more data an ML model is trained with, the more accurate it will become.
Just like humans learn through training and practice, machine learning models are also trained by feeding them with huge volumes of data.
One of the reasons Google is still the best search engine is because it has a lot of data compared to its competitors, including Yahoo and Bing (Microsoft’s search engine). With this data, Google is able to give users the best search results that match their search queries. Several other web apps also rely on data annotation to improve their algorithms in order to enhance their users’ experience.
So, what is data annotation?
Data annotation refers to the process of categorizing and labeling information or data so that machine learning models can use it. The data used to train machine learning models has to be accurately labeled and categorized for specific use cases. For instance, the categorization and labeling of data to be used by a search engine ML model is different from a speech recognition ML model.
Data annotation involves assessing four primary types of data; text, audio, video, and image. This article will focus mainly on images and texts annotation since they are the most popular types of data used to train machine learning models.
Text annotation
A 2020 State of AI and Machine Learning report shows that over 70% of companies relied on text to train their AI and machine learning models. The common types of annotations used with text include; sentiment, intent, and query. Let’s discuss each of these in detail.
Sentiment Annotation
Sentiment annotation involves assessing emotions, attitudes, and opinions, making it crucial to have the proper training data for machine learning models. Sentiment annotation is done by humans because it involves moderating content and sentiments on platforms such as social media and eCommerce sites.
Query annotation
This type of text annotation involves training search algorithms by tagging the various components within product titles and search queries to improve the relevance of search results. Algorithms that use query annotation are usually found in search engines for eCommerce platforms.
Intent annotation
This type of text annotation involves training machine learning models to identify intention in a particular text. Intent annotations help ML models to differentiate various inputs into categories, including requests, commands, bookings, recommendations, and confirmations. This type of text annotation is mainly used to train search engine Machine Learning models.
Image annotation
Image annotation involves training machine learning models with several images to help them learn about the features in those images. Some of the applications that use such algorithms include; computer vision, robotic vision, and apps that have facial recognition functionalities.
For effective training of ML models with image annotation, metadata has to be attached to all the images used. This metadata usually includes identifiers, captions, and keywords. Some of the popular use cases that take advantage of image annotation include; health apps that auto-identify medical conditions, computer vision systems in self-driving cars, machines used for sorting things, and many more.
Image annotation is more intense and requires more computation power than text annotation. This is simply because images carry way more data than texts. Training ML models with images involves learning about all the pixels in the various images fed into the ML model.
Images annotation has five main types, and these include;
Bounding boxes annotation
With bounding boxes, human annotators are tasked to draw boxes around specific subjects within the image. This type of annotation is mainly used to train autonomous vehicle algorithms to detect objects such as road labels, traffic, potholes, etc.
3D cuboids annotation
This type of image annotation involves drawing 3D boxes around specific objects in an image. Unlike bounding boxes that only consider length and width, 3D cuboids include the height or depth of the object.
Polygons
At times some objects may not fit well in a bounding box or 3D cuboid because not all things are rectangular. Objects such as cars, humans, and buildings are usually not perfectly rectangular, so they can’t fit in a rectangle or cuboid. In this case, human annotators have to draw polygons around the non-rectangular objects before feeding this data to an ML model.
Lines and spines
These are used to train machine learning models to identify lanes and boundaries. So, annotators are required to draw lanes between certain boundaries that you would wish your ML model to learn.
Semantic segmentation
This is a much more precise and deeper type of annotation that involves associating every pixel in a given image with a tag. This annotation type is mainly used in machine learning models for autonomous vehicles and medical image diagnostics.
What do data annotation companies do?
One of the major challenges involved in training machine learning models is finding the right quality and quantity of data to feed them. Remember, the quality and amount of data you provide these models determine the overall outcome of the tasks these models will be finally be deployed to do.
To help fix these issues, data annotation companies avail the appropriate amount of data that can be used to train various types of AI and ML models. These companies use the human-assisted approach and machine-learning assistance to provide high-quality data to train AI and ML models.
Besides providing training data for AI and ML models, data annotation companies also offer deploying and maintaining services for AI and ML projects. These are follow-up services meant to ensure the provided data provides the desirable results wherever the ML algorithm trained using this data is deployed.
For instance, if it is a search algorithm deployed in an eCommerce site, the data annotation company has to ensure the algorithm provides the best search results for the various user queries.
Data Entry and Data Annotation Jobs
The requirements are very different depending on the task that has to be done. Some of them are also data entry jobs that are not used to train any AI system at all but just to feed a software system with the right data.
Data Annotation Specialist
As already described earlier the actual labeling or annotation task is performed by humans. There are different job titles such as data annotation specialists, data annotator or data labeler. All of them refer to the human annotating the data. Depending on the industry and the requirements these jobs can pay anything from minimum salary of a few dollars per hour to higher amounts for annotating medical images or other difficult data.
For some of these jobs such as data entry there is often also no prior work experience required.
Data Entry
Data Entry are among the most popular jobs in this domain. The tasks can vary between digitizing documents, adding new products to a catalogue to manually copying information between two software systems. There is also a high chance that you find a data entry job with no job experience. Depending on how sensitive the data is you might also work from home to do the job. If the task does not require an internet connection and the employer is fine you might also work completely offline. Some of these jobs can also be seen as a side hustle to make some extra money in the evening.
If you’re a US citizen you can find data entry jobs at some of the large companies. Most of them allow you to work from anything within the US such as Utah, NYC, Las Vegas, Houston and many more.
Check out our list of data annotation companies to learn more!