Wildlife Technology Platform

95% ML Infrastructure Cost Reduction for a Wildlife Technology Platform

A production ML pipeline was too expensive and slow for the level of image intelligence the product needed. The work re-architected inference around optimized model serving, cutting annual infrastructure cost by more than $1M while improving speed and reliability.

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$1M+ annual savings
95% cost reduction
6x faster inference

Business Problem

The existing ML processing path had high cloud spend, avoidable orchestration overhead, and latency that constrained product iteration.

AI Solution

Animikh redesigned the serving path with NVIDIA Triton, containerized deployment, model optimization, batching, cleaner monitoring, and a simpler operating model.

Outcome

$1M+ annual savings, about 95% cost reduction, 6x faster inference, and a more maintainable production ML path.

Technical Shape

The implementation focused on production constraints rather than demo-only wins: architecture, data realities, evaluation, inference behavior, deployment path, monitoring, and maintainability.

NVIDIA TritonPyTorchDockerAzureModel serving

Animikh combines exceptional technical acumen with a natural curiosity that consistently elevates the team work. His work directly influenced both the quality of our models and the robustness of our ML systems in production.

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Where this work maps into AVIC Labs offers

ML Infrastructure OptimizationMLOps ConsultantComputer Vision Consultant

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