Bridging the Air-Gap for ML Deployment in Agriculture

Agriculture can be a surprisingly complex industry that involves many edge devices and equipment in its processes. With autonomous tractors hitting the market from companies such as Deere & Co., the agricultural industry’s reliance on edge devices has continued to progress with time. Currently, many of these edge devices are utilized with a variety of ML models to aid in seed development, crop protection, intelligent spraying, and automated harvesting. However, despite this industry’s reliance on robots, sensors, and automated equipment, finding an ML solution that can work on edge devices in an air-gap environment remains an obstacle.

With an air-gapped ML deployment solution, agricultural enterprises would be able to deploy models without the need of being connected to an external network. This is an important feature for agriculture since it allows for MLOps functionality in rural and remote areas where there may be limited or no connectivity. Unfortunately, alternatives to traditional cloud-based deployment platforms that can function outside the cloud are few and far between.

No Service, No Deployment: Challenges of Agricultural MLOps…

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