By 2022, the global market for machine learning is expected to balloon to $8.81 billion, with an annual growth rate of 44.1%. But the performance of any machine learning model depends on the quality of the data that’s used to train it. And as the demand for machine learning increases, so does the demand for high-quality data annotation services.
Are you thinking about partnering with a machine learning data annotation company to produce training datasets for your next artificial intelligence project? Here’s what you need to know.
We’ve done the legwork and put together a comprehensive guide on the different types of data annotation services in machine learning and how they can help you improve data quality and unlock better performance.
What Are the Different Types of Annotation Services?
Every machine learning model is different. In the same way models can range from the type of algorithms they employ to the industries they serve, data annotation services have a wide variety of tools, techniques, and trained annotators to get the job done.
Most training data will include images, video, audio, or text. Each data format requires specialized annotation techniques.
1. Image Annotation Services
Computer vision is one of the fastest-growing subfields of artificial intelligence. From self-driving cars and autonomous drones to facial recognition software, computer vision applications rely on labeled training data.
One of the most common ways to create training data for computer vision is through image annotation. Labeled images can improve models of object detection, movement prediction, boundary recognition, and more.
Here are a few of the most common techniques that companies use to annotate images for machine learning:
- Bounding boxes
- Polygon annotation
- Landmark annotation
- Semantic segmentation
2. Video Annotation Services
Another method of data annotation that’s commonly used for computer vision is video annotation. For example, to train the algorithms responsible for powering an autonomous vehicle, data annotators may rely on frame-by-frame video labeling tools to annotate pedestrians, road signs, and other vehicles.
Video annotation is one of the most labor-intensive forms of data annotation — each hour of video data takes roughly 800 hours to be labeled. This makes sense when you break a video down by its frames — a 10-minute video contains up to 36,000 frames.
Video annotation relies on many of the same principles as image annotation, including bounding boxes as well as polygon and landmark annotations.
3. Audio Annotation Services
Many machine learning models are activated by the sound of a person’s voice, including in-car navigation systems, chatbots, and virtual assistants such as Alexa and Siri. Training datasets for speech recognition require specific techniques and tools to make sense of audio streams.
Here are a few services that data annotation companies often offer when it comes to audio annotation:
- Audio transcription
- Acoustic data classification
- Environmental sound classification
4. Text Annotation Services
Text annotation has just as many uses as image or video annotation, including applications such as virtual assistants, chatbots, named-entity recognition, keyword tagging, relationship extraction, and sentiment analysis.
These are a few of the services that data annotation companies usually provide for text data:
- Text data collection
- Text classification
- Entity annotation
Why Rely on Professional Data Annotation Services?
So, why do leading AI companies prefer to rely on professional data annotation services rather than in-house teams or crowdsourcing platforms? Here’s why.
- Get to market faster. If you’re like most AI project teams, the vast majority of your time is spent finding, structuring, and labeling data. Getting to market faster means training and deploying your machine learning model faster—which, in turn, means getting usable data faster. Data annotation services can meet deadlines without compromising on quality.
- Expand your resources. Most machine learning models require enormous volumes of what may be highly complex training data. The proper annotation of this data requires a trained workforce and a specific set of tools. Without either one, labeling large volumes of data can be difficult, time-consuming, and expensive.
- Save money. Keeping an in-house data annotation team can be expensive. Moreover, paying a data scientist upwards of $100,000 a year to perform relatively basic work is a waste of your most valuable resources. Outsourcing to a team of professional data annotators allows companies to focus on bigger fish.
- Quality assurance. Revisiting training data to find and correct a simple mistake in data annotation is an inconvenience that can quickly turn into a major setback. Quality assurance processes offer significant value that is often overlooked. Data annotation companies allow you to take a proactive approach to annotation errors.
Trusted AI Annotation Services for Machine Learning
Keymakr offers high-quality video and image annotation for machine learning. Give your machine learning algorithm the pixel-perfect training data it craves with our dedicated team of data annotators, industry-leading tools, and quality control workflows.