What Data Annotation Skills Are Must to Develop ML/AI Projects

Data annotation is a core area of machine learning model development. Over the years, the demand for skilled data annotators has increased due to a sharp spike in demand for AI tools and software. AI/ML model developer agencies constantly looking for candidates who have sharp data annotation skills. It's vital because it ensures the accuracy of their model outcomes.

If you are among such developers and working on AI/ML projects, I think this piece can genuinely help you. I'll highlight an ideal candidate profile who can excellently handle any complex data annotation tasks. Thus, it will help you select the right candidates for your AI projects too.

Let's start!

Data annotation, in simple words, is all about creating metadata for machine learning models. The quality of the metadata is all that matters in this. And to maintain accurate quality and bring that data to a top-notch level, hard data annotation skills are a must. But wait, soft skills are valued too to maintain the quality consistently throughout the project.

Hard skills, I mean, the technical skills, which are also the basics. It'd be like the annotator must have some idea about different programming languages; but still, some ML projects do require an in-depth level of understanding. Besides technical skills, keyboard proficiency and quick information-processing abilities also come under the non-technical matters.

Anyway, let's point out the key technical skills which are essential for a data annotation to have.

Identifying each element included in the image or video and then labeling them with accurate meta information is the primary job that a data annotator must be good at. Along with objects, they have to identify various attributes and relationships associated with the image and videos. It includes drawing bounding boxes, polygons, or cuboids around objects stated in the frame and labeling them with the right metainformation.

Besides images and videos, annotating and labeling text data is one of the vital data annotation skills. Here, data annotators have to select specific text from used cases and accurately label and put metadata into that. Not that easy, it involves text categorization based on sentiment analysis and other various metrics like name, place, date, etc.

Identifying voice data and convert into machine learning codes is the primary job of a voice data annotator. Data annotators recognize the parts of speech and allot them labels. They listen to millions of audio clips based on the language they know and bring out data from that. They must understand the tone of the voice and identify the sentiment of the audio clips.

When annotating data on a large scale, proficiency in Structured Query Language (SQL) is a must. It's one of the key data annotation skills that help annotators efficiently retrieve necessary information from data pools for further analysis. Plus, annotators must have the ability to manipulate data using SQL to level up the labeling and annotation needs.

Understanding of programming language of data annotators helps ML model developers to set custom annotation rules. Annotators, those having programming backgrounds, can streamline the work quickly and enhance productivity.

Accuracy of the annotation is the ultimate thing that every annotator must go upon. If the output of the ML model comes wrong, it can tamper with the entire landscape of the project. Therefore, when starting the ML model development project, having a positive mindset is important for data annotators. But sometimes, the developers don't get them. At this time when outsourcing data annotation services becomes useful as it supplies the right data annotators to the right projects.

However, having the right attitude along with key data annotation skills makes them special and a useful resource for ML model development projects. The following things a data annotator must possess and ML companies must verify before hiring the candidate;

Data annotation, as a task, is constantly changing and turning new paths. The methods used in earlier models are now completely useless. So the person who is working in the annotation field must know how to do data annotation in changing time. It means they must have the knack to update their skills over time based on their current needs.

When you check only the hard data annotation skills, per se the technical skills, you only choose the ability of the person for doing your projects. However, when you consider soft skills besides hard skills, you ensure that person perfectly fits your specific project. In other words, you choose the person who is comfortable working on your project as per your instruction.

Lastly, I want to say you can also trust any outsourcing company if you are finding it difficult to manage annotation at some point. Data annotation companies like AskDataEntry can do many things for you. If you need, you can contact here.