Data annotation in machine learning importance? You are bored just by the sound of it right? Well, this is what’s causing breakthroughs in modern machine learning and artificial intelligence domains.
In our today’s feature we are going to talk about the following:
- What is data annotation machine learning
- What are the benefits of data annotation
- Data annotation importance
- Data annotation tools
Let’s know these together!
What is Data Annotation Exactly?
Put simply, data annotation is the process of labeling data for machine and deep learning. If you don’t label your data then even the most advanced machine learning and AI algorithms become useless for you. It acts as a bridge between you and the computers for smooth interaction.
Data annotation importance is known in the supervised and unsupervised machine learning techniques. But, we want you to know its significance in the market too. We want to share some statistics with you before we dive more into the data annotation importance and data annotation machine learning tools.
You already know that the more data you have, the more reliable end-products will be. But, the collection of such huge data isn’t peanuts. Multiple surveys demonstrate that 80 percent of AI project development time is exhausted from preparing the data. Data annotation importance says even the minor mistake could show to be catastrophic. This is why as humans we rely more on computers, specifically on data annotation tools.
The first step to data annotation for machine learning projects is to collect and aggregate heaps of data. This is the most crucial step as it’s not easy to source relevant data. Two of the most common ways to source out data for annotation are from web scraping or manual collection.
The job of human data annotators is tedious but they provide hard work for machines to engage with the users. The whole concept behind all of this hard work is to minimize human intervention in any way possible.
Types of Data Annotations
1- Image Annotation
To explain this better let’s assume you want a computer to recognize some pictures of dogs. Dogs come in different shapes, colors, and breeds, right? But a computer cannot process this information unless it’s as smart as a human who already knows about half of the breeds, colors, and sizes. Data annotation importance comes here where thousands of dog pictures are added in an ML-based computer system for it to memorize and analyze those pics at once.
So basically what’s happening is that you’ll not only be telling a computer what a dog is but will show thousands of pictures of dogs as examples for it to figure their shape, color, and breed on its own.
It plays a vital role in medical science, mainly. All the advanced machine learning software that the hospitals have today that can recognize cancerous tumors on CAT scans or MRI in seconds is made possible all because of data annotation tools.
2- Video Annotation
Ever wondered how autonomous driving is getting successful day by day? It’s not only because of the advanced artificial intelligence or machine learning algorithms but it’s also about their video annotation details.
We can talk about machine learning annotation importance here as well.
To train the neural networks of self-driven cars thousands of videos are annotated with tags like persons, cars, trucks, lanes, traffic signals, and any other obstacle to make the model aware of what it has to do with those tags.
Although Tesla, the pioneers of self-driven cars has shifted to unsupervised learning, their beginnings and standing pillars have been through supervised learning in ML which was made possible with data annotation tools.
3- Text Annotation
Text annotation is really important because it makes the computers flexible to understand the human language. Nowadays companies are annotating slang too. It’s been more than a year now since Facebook and Instagram came up with their text translation features. As both the social platforms are global, it has become important even to understand the slang in other languages.
Both the providers relied heavily on text data annotation tools for their machine learning software to work efficiently. When this feature was announced the translation didn’t even make sense but if you use it now it translates the slang in perfect English for you.
4- Audio Annotation
Let’s take Shazam, the app as an example. It’s an excellent music-recognizing app, just perfect for scratching that itch from a tune stuck in your mind.
Have you ever wondered how this app achieves it? 2 words, audio annotation!
Each track is labeled to give you a match from its database. This is possible with all of those data annotators working at the backend.
Data Annotation Tools
Let’s share with you the top 8 data annotation tools that are used worldwide.
- PDF Annotator
- Drawboard Projects
For any machine learning project to work and be successful it has to be fed with labeled data which is why data annotation tools are significant in ML.
Since data annotation importance is known to everybody now, it’s time you start working on AI projects and carefully choose your service provider.
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