Data collection for AI
For Artificial Intelligence applications to reach their full potential, they require large quantities of high-quality data. In some cases, organizations may already have access to the data they need to train their AI solutions; the data just requires high-quality annotation to be effective. However in other cases, companies need to collect additional data to ensure a healthy data pipeline that will support their AI deployments, whether it be for training, testing, or evaluation purposes.
Collecting data at scale is a challenging undertaking, particularly in light of privacy laws and other current regulations. In addition, when data is required from locations around the globe, it becomes increasingly labor-intensive to succeed at a large-scale or complex data collection effort. For these reasons, working with an experienced partner can significantly accelerate the creation of reliable data pipelines and help organizations move from pilot to production with greater speed and confidence.
LXT for AI data collection
Data collection methods
Data types include:
Augmented Reality and Virtual Reality (AR/VR)
Automated Speech Recognition (ASR)
Optical Character Recognition (OCR)
Our data collection services include:
Custom image and video collection
Domain-specific text creation
Script generation through crowdsourced data collection
Utterance and wake word collection
High-quality data annotation
Once data is collected, annotation allows the AI system to understand the context of the data and use it to make accurate predictions, solve problems and more. LXT provides end-to-end solutions where we collect the data, as well as transcribe or annotate it.
According to Statista, global data creation is projected to grow to more than 180 zettabytes by 2025. With this exponential growth and the behavioral changes that this reflects, the machine learning models powering your AI solutions may need weekly or even daily training. As a result, teams building AI solutions need to collect and annotate data on a regular basis to capture evolving trends in human behavior.