Achieving Accuracy with your Training Dataset
How do we make sure our training data is more accurate than the rest? Partners like Supahands eliminate the headache that comes with labeling work by providing end-to-end managed labeling solutions, completed by a fully managed workforce that is trained to work on your model specifics.
By Mark Koh, Co-founder and CEO, Supahands
Machine Learning (ML) and Artificial Intelligence (AI) spending is expected to hit close to $98 billion dollars by 2023, causing the ML and AI landscape to become increasingly competitive as new and improved models hit the market. To stay ahead, it is no longer enough for models to just ‘do the job’ as the industry is now valuing precision, giving the model with the highest accuracy the competitive edge.
“Garbage in, Garbage out” — The accuracy of ML and AI models ultimately rely on the quality of their training data. Therefore it’s no surprise that the data labelling market is expected to grow threefold to $5bn by 2023.
So, if accuracy is the secret ingredient to success in ML and AI models, how do we make sure our training data is more accurate than the rest?
Ensuring accuracy in your Training Datasets
A major factor that contributes to the quality of training data is the Quality Control methods used to ensure that labels are correctly annotated. Two reliable Quality Control methods are Ground Truth (correct and known answers that are interjected into the question pool at random intervals to ensure the ‘labeler’ is alert and high-performing) and Consensus (commonly used for subjective answers, where the majority agreed upon answer rules).
Labeling your data for ML and AI learning
The process of accurate data labeling is a very labour-intensive and time-consuming job when done in-house, as ML and AI models (especially the ones trained under Supervised Learning) often require thousands of images to be labeled. This increasing demand for quality training datasets prompted the emergence of data labeling companies that are providing data scientists with a hassle-free way to acquire accurate data.
Partners like Supahands eliminate the headache that comes with labeling work by providing end-to-end managed labeling solutions, completed by a fully managed workforce that is trained to work on your model specifics. With various quality assurance and control measures in place such as Ground Truth and Consensus, Supahands provides various types of labeling such as image annotation, transcription and sentiment tagging, and delivers high accuracy rates depending on the nature of your project.
With the accuracy rate in check, Data Scientists have a key ingredient they need to build competitive ML and AI models. It can’t be stressed enough that good data labeling leads to better results that would propel your ML & AI model forward with higher accuracy.
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