The adoption of artificial intelligence is growing at an exponential rate. While AI has come of age and it is becoming ubiquitous across the marketing ecosystem, AI operational efficiency largely depends on the data quality. The primary activity for AI/ML implementation is to prepare datasets that the model will ingest and learn from. It requires a label to be attached to each data piece in a dataset to enable the ML model to learn from it. The process of data labeling is very tedious and time-consuming. However, as technology is advancing, manual processes are being accompanied and replaced by automated labeling processes to improve AI productivity and efficiency.
Accurately labeled data is required to create the highest-quality ML models. In data labeling, various objects like videos, images, audios, and more are identified in raw data, and it is tagged with labels to help the ML models to make accurate predictions and estimations. You need to have a quality assurance process in place for checking the precision of the labeled data before feeding the algorithm and training the model. You can outsource your data labeling needs to firms that specialize in these tasks. Outsourcing helps to manage your data labeling service cost as compared to setting up an in-house team which requires building up the proper labeling infrastructure, talent acquisition to perform the tasks, and incurring additional costs for workforce management. Outsourcing provides access to data labeling services at competitive pricing to meet your customized requirements. These firms have the right data management tools, infrastructure, and annotators to manage your data labeling needs. Moreover, you can scale the services along the line to meet seasonal surge that requires you to label greater volumes of data.
Since the accuracy of your ML model has a direct association with the data quality, you need to adopt certain best practices to build a reliable ML model.
Proper Dataset Collection
Since data is at the core of the ML model, you need to employ a quality-backed approach to gathering the data. Depending on the domain you are focused on, you need to gather as much data as you can. Consider if the database contains enough representative data that will enable the ML model to extract patterns. The data should not focus on one type of information or a single data source. Greater diversity leads to a better representative and accurate outcome. It facilitates the ML model to infer in multiple real-world scenarios, and maintain specificity to reduce the chances of errors. Accuracy in data labeling measures how well the labeled features are consistent with real-world conditions. Conducting proper bias checks is necessary to enhance the ability of the model to deliver accurate results. Low-quality data can backfire twice, primarily during model training, and next when the ML model consumes the labeled data for informing decisions. Hence, preparing robust datasets is important for facilitating high-performing ML models.
Develop Proper Annotation Approach
Once you get through the data collection stage, you need to move into the data collection process. The most common data labeling approach includes in-house data labeling, crowdsourcing, outsourcing, and machine-based annotation. Machine-based annotation makes use of labeling tools and automation to increase the data labeling speed while maintaining the quality. Depending upon your capability, you can choose the right data labeling approach that helps you to maintain both costs and quality. You need to adopt a solid tagging taxonomy that is unique to your business. A flat taxonomy is suitable for lower-volume data, while a hierarchical taxonomy caters to large data volumes. The data labelers need to have an understanding of the industry-specific or complex language for tagging the data. You need to evolve your tagging structure over time to avoid any confusion.
QA Checks
You need to integrate a quality assurance method into the data labeling process to improve the accuracy of the labels. You need to include a round of QA for ensuring if the taxonomy is appropriate and human labelers are aligned with the labeling framework. It helps in preventing false labels from being fed to ML algorithms. There are few ways in which you can integrate the QA method. You can include ‘audit’ tasks among regular tasks for assessing the data labeler’s work quality. For targeted QA, you need to prioritize and review work items containing disagreements between labelers. Random QA allows you to regularly check a random work item sample for testing the work quality.
Follow Proper Guidelines
To avoid any mistakes during the labeling process, you need to prepare a guideline for data labeling. The guideline should include the various steps in the annotation pipeline that enables the labelers to move strategically. The team of labelers should maintain a line of communication to effectively convey and collaborate and move towards the target. Regular feedback facilitates a better understanding of the guidelines to achieve high-quality outcomes and deliver the project on time.
To Conclude:
These are some best practices that help to drive better results in the data labeling process and enable the ML model to perform better. It will enable you to maximize efficiency, minimize delivery time, and ensure a seamless flow across the data labeling pipeline.