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At present, Generative AI has entered the area of business and doing many things in the mainstream. To get the best of it, companies like Open AI, Dall-E, Midjoruney, etc are making their Machine Learning (ML) framework super strong and becoming more competitive. As a result, data annotation for machine learning is getting more advanced and complex with time.
At the beginner stage, while developing the ML models, many AI-developing companies have faced zero to no positive results. Do you know why?
Mostly, these companies tried to feed the ML model with poor-quality data. As a result, the model didn't perform very well and lost public interest. Big companies like OpenAI or Midjorney have become successful because they never ignore the quality of their data. They always prefer high-quality data aberration for machine learning model for their work. And, you know the rest is history.
OpenAI's ChatGPT has gained a wide sensation over time, and we come to know about the capacities of Generative AI through this. Data annotation is the core thing that boosts artificial intelligence to perform image recognition, identify objects from videos, generate texts, and so many other things.
Remember our childhood when our parents used to teach us different life lessons to us? That we are constantly applying in our lives to shape our opinions and perspectives about different things. Similarly, we annotate data to teach machines to generate results. Let's learn more about data annotation for machine learning in this detailed blog.
Let's start with the basics!
Data Annotation Definition
Data annotation is a conscious process of labeling raw data so that ML and AI models can interpret it. The data can be anything such as text, audio, images, or videos. For the development of AI/ML models, labeled data acts as a foundation. Based on the labels, machines recognize patterns, generate responses, make decisions, and so on.
Annotation is a broad thing and it includes various elements. For example, when annotating data, for images, you need to label each object of the image, and for text, you have to highlight and label key segments. Further, when it comes to audio files, you have to identify key sounds and keyframes for video files.
If we go into the details, data annotation for machine learning is complex and interesting. It involves image annotations where various techniques like bounding boxes detect different objects. Further, semantic image segmentation is there that annotates image objects with pixel-level accuracy.
When it comes to developing language-based AI, text annotation is crucial. Annotating text is important for sentiment analysis, named entity recognition, and natural language processing. On the other hand, voice data are being annotated for text-to-speech recognition. In video annotation, activities in each frame are recognized and labeled.
Well, annotation techniques for each type of data are different. It requires special skills to perform data annotation in machine learning models. In the next part, we are going to explore that.
What is the Definition of Annotating Data?
In short, data annotation is a specific task for labeling available data in an image, video, or text. We annotate data to help machines recognize and understand data in certain formats, information, objects, or patterns. So that, machines can perform tasks automatically, make decisions, and explore other possibilities of artificial intelligence.
Skills Needed for Data Annotation
Though annotation is a data-heavy complex task anyone can do it if they have the following skills;
- Strong focus & attention to details
- Good computer and software operating skills
- Ability to manage time
- Tendency to follow guidelines
- Be consistent at work
On the other side, technical skills like creating boxes and labels around the image or videos, can be taught. ML developers often train them with basic annotation work. But to perform data annotation in machine learning models perfectly, annotators have to put in some additional effort. They have to ensure that labeling is done perfectly with complete data accuracy.
How Data Annotation for Machine Learning is Important
Annotation and labeling are more like teaching your machines through data.
The sole purpose of data annotation is to teach your ML models the exact things that we want them to know. Teaching machines are more like humanized learning. Tell me how we used to teach a toddler about certain things? Presenting some flashcards in front of them helps. Similarly, machines need some labeled data to recognize things. Data annotation is the ultimate process that helps you train machines with labeled data.
Annotation helps simplify learning for ML models. However, training an ML model requires a huge quantity of annotated labeled data. AI/ML developers generally have some dedicated resources to perform all data annotation tasks. If they do not have, they usually outsource that.
Data Classification is Important
Data is everywhere. Everyone is generating data but there's a type of data that makes them distinguish each from other. Specifically speaking, there are two types of data you can have; either it is structured or unstructured.
Structured data mostly have a pattern and it's searchable by the computer. It has a specific pattern that makes it easily identifiable. For example, when you arrange a file in order and save it into your drive, it becomes structured data. So, you can easily find it through your search, and even, machines can easily find them for you.
Apart from that, unstructured data is different and far more complex and does not have any fixed pattern to track. Unstructured data lacks patterns that make it difficult to interpret. For example, social media posts, email replies, phone recordings, etc are unstructured data that do not follow any rule.
Data Annotation for Machine Learning needs Structured Data.
As we said, machines can only interpret processed data. It requires structured data to generate a response. To structure the unstructured data, you need to perform data annotation and data labeling. Unstructured data has much potential in machine learning fields. With the help of accurate data annotation, you can make all this happen. For that, you need some skilled data annotators for your work. Our next section is dedicated to talking about that part. Let's dig it.
What is Data Annotator and What he does
Data annotators are those who take charge of annotation work. Having some skilled data annotators is essentially important for performing annotation tasks. Besides skills, to perform data annotation for machine learning models, data annotators do the following essential things perfectly.
First, they label data. Data annotators tag and label data with relevant information. They identify objects in images, find nuanced text data, classify documents, draw bounding boxes around objects in images, and many other important tasks.
Second, they review their work. It's not just performing the annotation tasks for one time and handing over the project. Data annotators review their work minutely. They ensure the labeling and annotation work is done accurately. They check the completeness of each task, to be precise.
Third, they maintain records throughout. Annotating data for machine learning models is a complex task to perform. Data annotators have to maintain complex records securely for this task. Also, they store all changes made during the annotation process in their database.
Last but not least, they validate each output. The efficiency of the machine learning models completely depends on the quality of annotated data. So, data annotators completely validate each data point multiple times to check for accuracy.
What is Data Annotation Company and What Role it Plays?
A data annotation company like AskDataEntry can do amazing things for the development of powerful AI/ML models. For the last 9 years, we have delivered accurate data annotation for machine learning models. Plus, we have the right people and the right skillsets to perform our annotation work. Many companies in the ML/AI development sector have immensely benefitted from our services. We think our talent and expertise are our power and we play a beneficial role in the development of AI/ML models.
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