Scaling AI-Driven Agriculture with AWS SageMaker

Discover how we transformed a limited on-prem AI setup into a scalable, cloud-native platform on AWS. The result is low-latency inference, production-ready architecture, and a clear path to scaling 100+ machine learning models.

ClientAI-Driven Agriculture

PublishedFebruary 2026

IndustryAgriculture

On-prem AI often limits scalability and slows innovation. In this case study, we show how migrating CPU and GPU models to Amazon SageMaker enabled low-latency inference and created a production-ready foundation for large-scale agricultural AI.

Our customer is a technology company specializing in IoT and AI-driven farm management solutions that enhance agricultural efficiency and profitability. The company integrates scientific research into practical farming applications, providing advanced monitoring and decision-making tools using satellite imagery, drones, climatological data, and cultivation logs.

Diverse Model Requirements: The customer utilized two distinct types of models: a lightweight, CPU-based model (YOLO-based) for tree detection from aerial UAV images, and a heavy, GPU-based model (Mask R-CNN-based) for weed detection in vineyards.
Scalability for the Future: While currently operating a few models, the customer's goal is to scale up and create a comprehensive suite of potentially 10 to 100+ AI models.
Real-time Processing & Low Latency: The solution required real-time processing capabilities with low latency to handle approximately 500 to 5000 API calls per day.

A highly scalable, serverless AWS architecture to host and serve the customer's machine learning models seamlessly.

Amazon SageMaker for Scalable Inference: The core ML models were dockerized and successfully migrated to Amazon SageMaker. SageMaker Endpoints were deployed to host the trained models, providing automatic scaling and dedicated support for both CPU (ml.m5.large) and GPU-based (ml.g4dn.xlarge) inference workloads.
Serverless API Layer: An AWS API Gateway was implemented to route incoming HTTP requests and handle JWT token authentication. AWS Lambda functions act as the bridge, processing the API calls and securely invoking the SageMaker endpoints via the invoke_endpoint command.
Optimized Payload Handling: To accommodate SageMaker's payload limits for real-time endpoints (6 megabytes), an Amazon S3 bucket was integrated for the heavy GPU processes. Images are uploaded to S3, and the SageMaker endpoint retrieves them directly using the S3 URL for inference.
Monitoring and Container Management: Amazon ECR was used as the container registry for storing the Docker images used by the SageMaker models, while AWS CloudWatch was configured to collect metrics, processing logs, and monitor the overall health of the APIs and endpoints.

The Proof of Concept was a complete success, demonstrating that both the CPU and GPU models could be effectively migrated to AWS SageMaker with fully operational endpoints.

The prediction models yielded highly promising and measurable results:

Low-Latency Performance: Testing demonstrated excellent performance, achieving inference times of ~1-1.5 seconds for the CPU model and ~0.5-1.5 seconds for the heavier GPU model on g4dn.xlarge instances.
Production-Ready Roadmap: Alongside the functional PoC, we delivered a comprehensive 2-month migration plan utilizing an Infrastructure-as-Code (IaC) approach with Terraform. This plan included an AWS Organizations strategy encompassing isolated production and security accounts to ensure robust security and compliance.

The newly designed cloud-native architecture provides the customer with the scalable, reliable, and cost-effective foundation they need to expand their AI suite and continue driving innovation in the AgriTech sector.

AWS SERVICES

Amazon SageMaker

AWS Lambda

Amazon S3

Amazon ECR

Amazon API Gateway

Key Takeaways

~1–1.5 sec

Real-time inference latency (CPU)

~0.5-1.5 seconds

Inference of heavier GPU model

2 models

CPU and GPU workloads unified

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