Automated data labelling versus manual data labelling
For any AI- or ML-powered solution to truly succeed, it is critical that the model be trained via repetitive data annotation and labelling. For organisations, this process is not only time consuming due to the vast volumes of data involved but also complex and sensitive because accuracy and reliability are paramount.
Here’s discussing the differences between automated data labelling and manual data labelling and the better technique for tagging labels in contents that are available in a range of formats, including text, audio, video, image, and sensor data (from IoT devices).*
Automated data labelling
Automatic data labelling, as the term suggests, can help overcome the challenges that data annotation process presents. This type of data labelling relies on ML algorithms that can make sense of massive datasets. To ensure that there is no discrepancy, the algorithms can be improved by human input. While this model predominantly revolves around the automation of the entire process, the human-in-the-loop element is crucial to ensure quality control in terms of algorithm functioning and reliable outcomes. Efficient data labelling at scale becomes a challenge for organisations, and that is where automatic data labelling via AI systems can deliver more speed than a manual process.
Manual data labelling
In this model, the responsibility of data annotation lies with expert annotators. Once they are provided with raw datasets, their job is to deliver trainable datasets based on unique predefined parameters such as the project, required outcomes, and specifications. Businesses that depend on manual data labelling face several challenges. For starters, as we have already established above, data annotation is a time-consuming task (especially when done manually). The time taken could depend on other factors too, such as the tool and technique used, the number of items to be annotated, and the quality of data. To ensure complete accuracy and authenticity, the process and outcomes must also go through multiple quality checks and annotation audits.
Which technique is better?
While there is nothing inherently wrong with either automated or manual data labelling techniques, taking a hybrid approach is by far the most effective choice in terms of producing precise and time-efficient results. Manual data labelling takes up precious time and automated labelling can be quite risky (especially in the case of complex annotation tasks). In a best-of-both-worlds approach, which combines manual and automated labeling, the AI systems will take care of your labelling needs while human experts will be involved in validating the results. Once trained, intelligent machines can precisely annotate data without the need for any human intervention. Manual intervention, in this case, is only required for more complex tasks. This approach is definitely more cost-effective too. Ensuring the ideal blend of man and machine can also help improve the quality of labelled data.
How can Infosys BPM help?
Infosys BPM enables clients’ data science teams to build high-quality training data for AI at scale by deploying a platform and human-in-the-loop service model. Built to save time and resources, our proven service model leverages the power of human intelligence (humanware) and automation capability (software) to continuously churn out high-quality training datasets at scale for AI training and evaluation.
Our agile and platform-agnostic operating model helps us work with a variety of businesses by leveraging client-owned in-house tools, open-source platforms, or third-party tools.
Our team has specialised expertise in handling image annotations, work packages, and audio files for marquee brands and global clients across industries like CPG, retail, media, financial services, oil and gas, insurance, and healthcare.
Learn more about how Infosys BPM successfully combined human capability with state-of-the-art intelligent automation to increase the speed to market and deliver accurate training data output.
*For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive yet modular suite of services is doing exactly that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper- productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.