The Overview

In the Taxi business, AI has helped immensely in understanding customers’ needs, their route preferences, spending habits, and communication patterns to solve their commuting issues. On top of it, it has made the passenger experience safer and more customized than ever before. TMS wanted to accelerate the development of its app on these lines to provide its clients- taxi/commercial fleet owners a robust taxi booking platform.

The development envisaged the requirement of training data based on both Computer Vision and Natural Language Processing as per the client use case requirements. We helped them power driver monitoring systems and driver-passenger chat solutions with accurate training and data required for their ML models.

The Challenge

Driver monitoring systems in the market are still in their infancy. While performance and safety validation is paramount, there is a significant gap when it comes to effectively recognizing the complex and nuanced cognitive and emotional states of the driver. TMS encountered a major challenge of keeping up with the pace and costing of labeling its client imagery and conversational data which was in sheer volume and variety.

There is no one-size-fits-all solution that meets all the data labeling needs. The robustness and effectiveness of the algorithms depend on the availability of high-quality driver activity datasets. The challenge was to have a dedicated, highly skilled workforce continuously working on the job and assisting with client-specific data annotation.

The Solution

The team at AIsmartz helped TMS develop high-quality data sets with a feedback intensive communication structure. Two teams of 8 members each for imagery and video annotation, and one team of 5 members for tagging text, labeled vast numbers of CV and NLP datasets that TMS needed across its client use case requirements. Our labeling teams made focused shifts to keep business rules intact as edge cases came up, and adjusted taxonomy and quality rubrics to reflect the current training data objectives. We designed the labeling workflows to optimize accuracy and simplified the number of labels per image/video frame to eliminate errors.

The Results

By partnering with AIsmartz, the client found a dependable and elastic way to focus on building its technology. TMS became a long term partner as we took over the data labeling job and optimized the annotation process for them; freeing 60% of the precious time spent on wrangling data by their core team of data scientists. We ensured a consistent level of high-quality training data and eventually reduced our man-hours too for annotation, by leveraging ML-enabled labeling automation on our shortlisted data training platform.
At AIsmartz, we understand that different types of data have different labeling requirements. We incorporate the best data annotation platform and workforce practices to translate your data into a fuel that will drive your business towards success.

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