The challenge

An enterprise financial institution with high-volume mission-critical digital operations needed to deploy Artificial Intelligence models for credit scoring and fraud detection at scale. However, its existing cloud infrastructure was not designed to support for compute-intensive AI workloads. Limited access to GPU resources, inefficient orchestration, and non-optimized data pipelines led to slow training cycles and unstable inference performance. At the same time, cloud costs increased unpredictably due to the lack of visibility and control over high-consumption AI workloads, putting both scalability and operational efficiency at risk.

The solution

GIGA IT implemented an AI-optimized cloud architecture designed specifically for high-performance machine learning workloads. This included provisioning GPU-based compute environments with dynamic scaling, separating training and inference workloads, and redesigning data pipelines to enable faster and more reliable data access for AI models. We also introduced MLOps pipelines to standardize model deployment and monitoring, while integrating FinOps practices tailored to AI workloads, providing cost visibility, usage governance, and automated resource optimization across compute-intensive processes.

The results

• A cloud environment purpose-built for scalable AI workloads.
• Significantly faster model training and more stable real-time inference.
• Improved cost efficiency through optimized GPU utilization and FinOps governance.
• Reduced friction for launching new data and AI initiatives.
• Greater operational control and predictability for AI-driven workloads.

Explore Real-World Results