What is training data? Where to find it? And how much do you need?
Artificial Intelligence is created primarily from exposure and experience. In order to teach a computer system a certain thought-action process for executing a task, it is fed a large amount of relevant data which, simply put, is a collection of correct examples of the desired process and result. This data is called Training Data, and the entire exercise is part of Machine Learning.
Artificial Intelligence tasks are more than just computing and storage or doing them faster and more efficiently. We said thought-action process because that is precisely what the computer is trying to learn: given basic parameters and objectives, it can understand rules, establish relationships, detect patterns, evaluate consequences, and identify the best course of action. But the success of the AI model depends on the quality, accuracy, and quantity of the training data that it feeds on.
The training data itself needs to be tailored for the end-result desired. This is where Bridged excels in delivering the best training data. Not only do we provide highly accurate datasets, but we also curate it as per the requirements of the project.
Below are a few examples of training data labeling that we provide to train different types of machine learning models:
2D/3D Bounding Boxes
Drawing rectangles or cuboids around objects in an image and labeling them to different classes.
Marking points of interest in an object to define its identifiable features.
Drawing lines over objects and assigning a class to them.
Drawing polygonal boundaries around objects and class-labeling them accordingly.
Labeling images at a pixel level for a greater understanding and classification of objects.
Object tracking through multiple frames to estimate both spatial and temporal quantities.
Building conversation sets, labeling different parts of speech, tone and syntax analysis.
Label user content to understand brand sentiment: positive, negative, neutral and the reasons why.
Cleaning, structuring, and enriching data for increased efficiency in processing.
Identify scenes and emotions. Understand apparel and colours.
Label text, images, and videos to evaluate permissible and inappropriate material.
Optimise product recommendations for up-sell and cross-sell.
Optical Character Recognition
Learn to convert text from images into machine-readable data.
How much training data does an AI model need?
The amount of training data one needs depends on several factors — the task you are trying to perform, the performance you want to achieve, the input features you have, the noise in the training data, the noise in your extracted features, the complexity of your model and so on. Although, as an unspoken rule, machine learning enthusiasts understand that larger the dataset, more fine-tuned the AI model will turn out to be.
Validation and Testing
After the model is fit using training data, it goes through evaluation steps to achieve the required accuracy.
This is the sample of data that is used to provide an unbiased evaluation of the model fit on the training dataset while tuning model hyper-parameters. The evaluation becomes more biased when the validation dataset is incorporated into the model configuration.
In order to test the performance of models, they need to be challenged frequently. The test dataset provides an unbiased evaluation of the final model. The data in the test dataset is never used during training.
Importance of choosing the right training datasets
Considering the success or failure of the AI algorithm depends so much on the training data it learns from, building a quality dataset is of paramount importance. While there are public platforms for different sorts of training data, it is not prudent to use them for more than just generic purposes. With curated and carefully constructed training data, the likes of which are provided by Bridged, machine learning models can quickly and accurately scale toward their desired goals.
Reach out to us at www.bridgedai.com to build quality data catering to your unique requirements.
ai data, ai models, big data, content moderation, data, dataset, line annotation, ML, ml data, ml models, NLP, nlp data, nlp models, point annotation, sentiment analysis, technology, training data