QuickCode.ai won the 2021 DataTribe Challenge in early November 2021, for the company’s innovative approach to enabling organizations to unlock the insights trapped in text data to solve the data-labeling bottleneck that is inherent when deploying AI applications at scale.
“We’re excited to announce our latest investment in QuickCode.ai, winners of the fourth-annual DataTribe Challenge. Given our focus on over-the-horizon cyber and data science companies, Quickcode.ai is a great fit,” said John Funge, Managing Director at DataTribe. “We’re looking forward to working closely with Quickcode as they address maybe the most important part of machine learning: ensuring the highest possible quality training data and making sure models don’t drift over time”.
QuickCode.ai software helps solve this problem of getting the right kind of labeled training data, resulting in more accurate and less biased machine learning models. The solution uses its machine learning method to target and create datasets for text-based machine learning algorithms, focusing users on finding the most representative data, including the hard-to-label and edge cases. The platform makes it dramatically more efficient for experts to precisely identify the right training data, thereby improving label quality while reducing labeling time and ultimately resulting in more accurate models.
“This new investment empowers us to continue evolving and strengthening our research and development and processes to bring about new, exciting technologies for text-based machine learning and artificial intelligence for deploying production-quality machine learning models,” said Shannon Hynds, CEO of QuickCode.ai. “We’re honored and grateful for the opportunity to partner with DataTribe, which supports our mission of generating high-quality training data for machine learning and sees the great value of what QuickCode.ai has to offer to the competitive cybersecurity market.”
This relationship will open doors for QuickCode.ai’s long-term vision for the business itself to be used not only to improve efficiencies and accuracy in machine learning development pipelines but also to identify and mitigate model drift in operational pipelines. Specifically, as language changes over time due to technology advances, terminology evolution, or slang and jargon changes, QuickCode.ai can help identify cases that are missed by the now-outdated model.
QuickCode.ai adds to the feedback loop that informs the decision-making processes by making investments in the technology that helps users guide QuickCode.ai’s machine learning. These investments include visualizations, metrics, and enhanced recommending algorithms.