Importance of Data Annotation for Machine Learning

The word data annotation or data labeling comes when someone is talking about implementing an AI or ML project. So what is machine learning or artificial intelligence? The basic premise of machine learning is that computer systems and programs can become able to improve their outputs in ways that resemble human cognitive processes, without direct human help or intervention, to give us insights. In other words, they become self-learning machines that, much like a human, become better at their job with more practice.

This practice is gained from analyzing and interpreting more training data. The key to effective AI/ML implementations is “clean” labeled data.This labeled data typically comes in the form of training and test sets that will orient the machine learning program toward future results as future data inputs are added.

Data Annotation for Machine Learning

Data annotation is the process of labeling the contents recognizable to machines through computer vision or natural language processing based AI or ML training available in various formats.

There are several types of annotations:

• Image annotation — Annotating still images
• Video annotation — Annotating moving images
• Text annotation — Annotating written text, both types and handwritten
• Audio annotation — Annotating sound and speech
• LiDAR — Annotating the 3D Point Cloud produced by the LiDAR

This process adds tags to the data which acts as metadata to the dataset. These tags are used to train the model about different features of the data. For example, to train a self-driving car, thousands of images will be annotated with tags like person, car, trucks, lanes, traffic signals, and other obstacles to make the model learn about these tags and what they have to do like the output.

A data annotator’s job is to show the machine learning model what outcome to predict. In practice, data annotation is the process of transcribing, tagging, and labeling significant features within your data. These are the features that you want your machine learning system to recognize on its own, with real-world data that hasn’t been annotated.

Machine learning is dependent on the quality and quantity of its training data. Even though data annotation is very tedious and time-consuming work, it is necessary to the overall success of the project. In other words, when you have a good test and training data setup, the machine is able to interpret and sort new incoming production data in better and more efficient ways.

Key Steps in Data Annotation Projects

Sometimes it can be useful to talk about the staging processes that take place in a complex data annotation and labeling project.

The first stage is acquisition. Here’s where companies collect and aggregate data. This phase typically involves having to source the subject matter expertise, either from human operators or through a data licensing contract. Data collection is a critical process as it requires you to collect large volumes of data specific to your needs which might not be easily available. It can be either collected manually from different sources or can be scraped from the web and many other ways.

The second and central step in the process involves the actual labeling and annotation. This step is where different types of annotations like bounding box annotation, semantic segmentation,3d point cloud annotation, or NLP annotation like named entity recognition, categorization, sentiment, and intent analysis, etc would take place. There are many different types of data annotation, all of which suit different use cases.

These are the nuts and bolts of accurately tagging and labeling data to be used in machine learning projects that succeed in the goals and objectives set for them. After the data have been sufficiently tagged, labeled, or annotated, the data is sent to the third and final phase of the process, which is deployment or production.

TagX Data Annotation Services

Since data annotation is very important for the overall success of your AI projects, you should carefully choose your service provider. TagX offers data annotation services for machine learning. Having a diverse pool of accredited professionals, access to the most advanced tools, cutting-edge technologies, and proven operational techniques, we constantly strive to improve the quality of our client’s AI algorithm predictions.

With the perfect blend of experience and skills, our outsourced data annotation services consistently deliver structured, highest-quality, and large volumes of data streams within the desired time and budget. As one of the leading providers of data labeling services, we have worked with clients across different industry verticals such as Satellite Imagery, Insurance, Logistics, Retail, and more.

TagX has experts in the field who understand data and its allied concerns like no other. We could be your ideal partners as we bring to the table competencies like commitment, confidentiality, flexibility, and ownership to each project or collaboration. So, regardless of the type of data you intend to get annotations for, you could find that veteran team in us to meet your demands and goals. Get your AI models optimized for learning with us.

Originally published at https://www.tagxdata.com.