A guide to automated data labelling
With enormous amounts of information being generated every day, data is nothing short of currency. But raw data is of no use to modern applications such as machine learning (ML) and artificial intelligence (AI). Labelling raw data for use in these applications is time and labour intensive. But if we can automate the data labelling process, we can harness the power of data and help save costs and time compared with manual data labelling.
Automating the data labelling process
Automating the data labelling process is about automating the right things at the right levels. Although automated data labelling aims to eliminate the slow, tedious, and expensive elements of the labelling process, the actual scope for automation needs to be more nuanced. You cannot opt for an automation method that requires active human participation for each label, and you cannot avoid human intervention entirely from the data labelling process.
The key here is to automate the labelling process to the level of abstraction that neither puts the humans at the centre, overseeing each label, nor does it eliminates them from the process. Data scientists should be able to transfer their knowledge at a higher level - building labelling functions that capture the rationales instead of assigning individual labels - for the automated data labelling to work in practice.
Differences between manual and automated data labelling
ML and AI practitioners have primarily been labelling raw data manually. But this is a slow, tedious, and expensive process, which is prone to multiple pitfalls. There are three significant factors that differentiate manual and automated data labelling.
Scalability:Manually data labelling is time intensive and lacks scalability. There are limitations to the amount of data an individual can label. On the other hand, automated data labelling can save orders of magnitude of time. You can dedicate this time and resources to building and perfecting the models for better performance and accuracy.
Adaptability:Training sets must be relabelled when data drifts or new error modes are found or when requirements change. Manual data labelling necessitates several reviews of each data point to keep up with changing needs, which is a waste of time and resources. On the other hand, automated data labelling allows you to modify labelling parameters and functions, giving you a new training dataset at computer speed (not human speed), with minimal time and resource requirements.
Governability:In manual data labelling, there is no record of the thought process or conclusions that led to the categorisation of those labels in that specific manner, making it difficult to audit the labelling decisions. This can pose challenges in terms of quality control, safety, and compliance. Automated data labelling eliminates these challenges. You can trace every label back to a specific inspectable function, allowing you to remove any potential bias or other undesirable behaviour from your labelling process.
The benefits of automated data labelling
Although far from a standardised technology and capable of providing predictable performance across the board, automated data labelling can be helpful in the following ways:
Pre-annotate parts of your datasets:Automated annotation process cannot label everything; human review and intervention are still necessary after automated data labelling. However, it can significantly reduce the work a human annotator needs to perform by pre-annotating some or all of the datasets.
Reduce the workload for your team:Automated data labelling models can also assign a confidence level to the labels based on different factors, such as task difficulty or use case. This enriches the annotation dataset and reduces the workload for your team by only having to review or correct annotations with a lower confidence score.
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How can Infosys BPM help?
With Infosys BPM Annotation Services, you do not have to worry about manually labelling your data to build a high-quality training dataset for your ML or deep learning models. Be it text, image, audio, video or sensor data, a dedicated and expert annotations team can help you build high-quality and accurate training datasets with an automated plus human-in-loop service model.