Bridging the Air- Gap for ML Deployment in Agriculture
October 27, 2022
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
Alternatives to cloud-based deployment such as offline learning, where models are trained with batch data, can only be updated after learning whole data sets which are far less adaptable to dynamic data patterns compared to connected learning. But getting connections to rural areas for ML deployment is no small feat. Physical barriers like mountains in agriculture prevent a stable line of sight for a strong connection. Similarly, the water in nearby lakes and rivers is a key interrupter of wireless frequencies resulting in over 37% of rural America lacking internet connectivity.
Some enterprises attempt to maintain a connection to their agricultural edge devices via satellite to circumvent the hardwired issues. While satellite connectivity can work in remote and rural areas it still comes with issues like high latency and processing lags as data sets are transferred from dish to dish and server to server. Additionally, satellite services can be affected by harsh weather conditions and geographical limitations, making it a highly unreliable solution for agricultural ML. This is leading agricultural enterprises to search out air-gapped deployment solutions for their edge devices as the associated cost for overcoming the hurdles of cloud-based deployment in these areas can strike a serious blow to your budget.
Filling in the Air- Gaps of Deployment on the Edge
Agricultural enterprises are in a dire need of a high-performance ML model deployment platform designed to support devices on the edge even in air-gap environments, allowing agricultural enterprises to run, monitor, and observe their latest ML models even in remote areas.
Wallaroo enables model deployment at the edge so companies with operations in rural areas have the ability to run the models for their equipment despite a lack of internet connectivity. It further provides real-time low-latency processing and monitoring of data by offering a high-quality air-gapped solution for edge devices. This allows for experimentation like A/B testing and data validation, thus minimizing potential risks in your agricultural models.
Agriculture has long been a major source of innovation and production, but the industry has faced challenges in applying MLOps on edge devices in air-gapped environments. Wallaroo has provided the ultimate solution for this, allowing for offline deployment of models as well as real-time analytics that set its users far above their competitors. This is certainly a major win for the agricultural industry with operations in remote areas and limited internet connection.
If you are in the agricultural field and are looking to modernize your current processes with Machine Learning, contact our specialists to learn more about the air-gap solutions for edge devices that Wallaroo has to offer.
