Case Studies | SaaS B2B

Taking rainforest disease detection from laptop to cloud

Rainforest Disease Detection ML Deployment

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The engagement had three focus areas.

Getting the model off a local machine

The existing model was hosted locally, which limited usage and reliability. It needed to be stood up in Amazon SageMaker and deployed as an endpoint so it could be called dependably and at greater scale.

A secure home for imagery and model artefacts

Drone imagery and model artefacts needed secure, well-permissioned storage that the training and inference workflow could draw on, laying a clean data foundation for the model.

A path to keep improving the model

Standing up the model once was not enough. The client needed architecture that would let them retrain future models, and the SageMaker skills to continue development themselves.

Solution

Delivered under Cloud Combinator's Cloud Accelerator, the work moved from a secure data foundation, through deployment, to retraining and handover, with the client's team involved throughout.

SageMaker

Existing model deployed as a live, scalable endpoint

Drone

Rainforest canopy disease detection, now cloud hosted

Retraining

Architecture set up to improve future models

By the numbers:

  • SageMaker - Existing model deployed as a live, scalable endpoint
  • Drone - Rainforest canopy disease detection, now cloud hosted
  • Retraining - Architecture set up to improve future models
Changes

The client's model moved from local hosting to a deployed SageMaker endpoint on AWS, with a secure data foundation, a retraining path and a team upskilled to carry development forward.

  • Model in the cloud — The existing local model stood up in Amazon SageMaker and deployed as an endpoint, removing the limits of local hosting.
  • Secure data foundation — Amazon S3 buckets with appropriate permissions for imagery and model artefacts.
  • Built to improve — Architecture to enable future model retraining as the client gather more data.
  • Team upskilled — A hands on SageMaker workshop and general support so the client can continue development themselves.
  • Handover — The project delivered with a full explanation, and SageMaker confirmed as a suitable environment for the product to grow in.

With their model running on SageMaker and a retraining path in place, the client are positioned to scale their rainforest monitoring service, improve accuracy over time and draw on the wider AWS toolset as they grow.

AWS Stack

Amazon SageMaker

For hosting and deploying the disease detection model as an endpoint.

Amazon S3

For secure storage of drone imagery, training data and model artefacts.

Amazon SageMaker

Training capabilities for the architecture supporting future model retraining.

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