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•Case study
Cloud-Based AI & ETL Platform for Agricultural Machine Learning
A cloud-native system capable of orchestrating AI workloads, managing data pipelines, and storing results for an AgriTech company specializing in machine learning for field crop analysis.
AgriTech
Israel
3 months
$30K
•Case study
Our Approach
From the start, we approached this engagement as an AI infrastructure project, not just a backend implementation.
Our focus was on: Creating a robust execution layer for AI and data pipelines, capable of handling compute-heavy jobs.
Designing a modular system architecture on AWS that could later be adapted to other cloud providers if needed.
Supporting the full lifecycle of AI workloads, from data ingestion to execution and output delivery.
Introducing strong observability and control mechanisms for background jobs and long-running processes.
Working in tight collaboration with the client's technical leadership through structured sprints and regular reviews.
Rather than over-optimizing for a single model or workflow, we built a flexible foundation that could support evolving AI use cases in agriculture.

The Problem
The client needed a centralized system to: Trigger and manage ML and deep learning jobs in a consistent way.
Process and store large volumes of agricultural data efficiently.
Work with both relational and non-relational data stores, depending on the workload.
Schedule and monitor long-running jobs without manual intervention.
Ensure the platform was stable, maintainable, and production-ready.
Without this foundation, scaling AI solutions across different products and datasets would have been difficult and costly.
AI-Driven ETL and Workload Execution
Scalable AWS Infrastructure
Job Scheduling, Queueing, and Visibility
CLOUD-BASED AI EXECUTION PLATFORM
We delivered a cloud-based AI execution platform with the following capabilities:
KEY FEATURES IMPLEMENTED:
- 01AI-Driven ETL and Workload Execution
The system allows users to submit jobs via APIs, execute data-intensive AI workloads, and automatically persist results for downstream use. This created a repeatable and reliable workflow for running ML models in production.
- 02Scalable AWS Infrastructure
We built the platform using core AWS services such as EC2, AWS Batch, Lambda, S3, RDS (PostgreSQL), and DynamoDB. Each component was selected based on performance, scalability, and cost considerations.
- 03Job Scheduling, Queueing, and Visibility
Background jobs are scheduled and queued automatically, with monitoring in place to track execution progress, performance, and failures. This gave the team clear insight into system behavior at all times.
- 04API-First System Design
All core operations, including data upload, job execution, and result retrieval, are exposed through APIs. This made the platform easy to integrate with existing ML pipelines and future applications.
- 05Operational UI
We also delivered a lightweight user interface built with a modern frontend framework, allowing non-infrastructure users to monitor jobs and system status without touching cloud resources directly.
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