Accelerating Growth & Revenue in Cold Chain with Edge Machine Learning

With assets moving through areas with low connectivity for prolonged periods of time, performing more computation directly on device — is key.

2022 10 18 15 38 57 Cold Chain With Edge Machine Learning (002) pdf (1)

*This content brought to you in partnership with Edge Impulse*

With assets moving through remote environments or areas with low connectivity such as airplanes or sea carriers for prolonged periods of time, reliability and power efficiency are of paramount importance. By bringing more intelligence to the edge, meaning the utilization of  machine learning capabilities locally on device, solutions can work efficiently for months, even without cloud connectivity.

Edge solution providers are enabling companies to develop intelligent, reliable, and cost-effective cold chain monitoring solutions. Platforms like Edge Impulse are democratizing machine learning development and making it easier to build custom edge machine learning solutions, through a low-to-no code machine learning ops (MLOps) platform. The company’s focus is helping customers put into production effective cold chain monitoring solutions with innovative new edge machine learning techniques.

Read this case study to learn how a cold chain sensor solution provider has partnered with Edge Impulse to build an edge machine learning solution to track whether a package had been exposed to high temperatures, been dropped, or shaken, ensuring higher quality results for its logistics partners.