Manufacturers and consumers of perishable goods, such as pharmaceuticals, food, and beverage products, are demanding deeper insights into how products are being stored and transported. It is therefore becoming increasingly critical that companies can track and control the temperature of assets through the logistic network known as the cold chain.
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.
Please CLICK HERE to download the white paper.
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