Machine Learning for Industrial

Use AI and ML to enhance visual imaging accuracy earlier in the quality control process. Automating visual inspection with machine learning can help industrial organizations with use cases such as detecting part damage like dents or scratches, identifying missing components, or even uncovering process issues such as recurring patterns of defects.

Automate supply chain optimization tasks that traditionally require human inspection or oversight, such as tracking throughput or the transportation of goods—even in edge environments with limited internet bandwidth. Computer Vision (CV), the application of machine learning used to analyze digital images or videos, can be added to existing on-premises cameras for a wide variety of industrial applications, and used to make ML-based predictions locally with high accuracy and low latency.

Automatically identify potential safety issues such as when workers or forklifts are straying into dangerous, off-limit zones, or other instances of non-compliance in industrial spaces that can lead to accidents. Implement Computer Vision at the edge for fast response times to send an alert to an individual, or automatically shut off machinery.

Model changes or irregularities in the supply chain, re-evaluate demand, optimize performance, and adapt to product or distribution changes. A machine learning-powered forecasting solution combines time series data with additional variables to automatically examine complex relationships and build forecasting models that are up to 50 percent more accurate than looking at time series data alone.