Data & AI platform

Agricultural intelligence platform

Cloud infrastructure, geospatial workflows, analytics and machine-learning services operated as one production platform.

Sector: Agriculture technology · Focus: Geospatial data and platform operations · Scope: Cloud, data, ML and FinOps

The challenge

Different workloads, shared operational controls

Application services, satellite and geospatial processing, distributed data jobs, analytics and machine learning all had different technical profiles.

The operating model brought those workloads under shared environment, security, observability and cost controls.

Delivery approach

Cloud platform

Repeatable environments, networking, access and deployment foundations.

Data workflows

Geospatial processing, orchestration and governed analytical services.

Operations

Monitoring, incident response, cost review and continual platform improvement.

What changed

  • A common operating model across application and data workloads.
  • More visible reliability and cost across the platform.
  • A production foundation for agricultural decision-support services.
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