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How Training Data in Machine Learning is Used to Develop an AI Model?

Training data is the real fuel to accelerate the machine learning process. It can only provide the actual inputs to the algorithms to learn the certain patterns and utilize this training to predict with the right results if such data comes again in real-life use.

Actually, training data is generated through labeling process which involves image annotation, text annotation and video annotation using certain techniques to make the objects recognizable to computer vision for machine learning training.

Labeling such data is called “Data Annotation” done by the well-trained and experienced annotators to annotate the images available in different formats. The best tools and techniques are used to annotate the object of interest in a image while ensuring the accuracy to train the popular AI models like self-driving cars or robotics.

How Data Annotation is Done?

To annotate the data the combination of humans and machines are used at large scale for mass production and achieve the economies of scale. To annotate the data bulk of images are uploaded on the software servers and then given access to the annotators for annotating the each image as per the requirements.

Types of Data Annotation

The data annotation service basically divided into three categories text, videos or images. And annotation job is done as per the AI project training data needs and algorithms compatibility to utilize the information from such labeled data.
Image annotation is used to train the computer vision based perception model. And there are different types of techniques adopted in this image annotation service. Bounding box, semantic segmentation, 3D cuboid, polygons, landmark annotation and polylines annotation are the leading image annotation methods used in such tasks.

How Training Data is used in Machine Learning?

Machine learning training is performed through certain amount of data from the relevant field. And to train the AI model through machine learning certain process is followed that involves acquiring training data, using the right algorithm, validation of the model and implementation to check the prediction results in real-life use.

In fact, in machine learning labeled or unlabeled training data is used as per the supervised or unsupervised ML process. In supervised machine learning the objected is categories, classified and segmented to make it recognizable to machines.

While in unsupervised machine learning, the data is not labeled and algorithm have to group the object as per the understandings to group them accordingly with its own segmentation to recognize the same when used in future. 

How to Acquire Training Data for Machine Learning?

Machine learning training is possible only when you have quality data sets, and acquiring the training data is one the most challenging job in AI world. But if you get in touch with companies like Cogito, you can get high-quality training data for Artificial Intelligence your machine learning project with best level of accuracy for right prediction.

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