12 weeks
Timeline
Technology
Industry
6 engineers
Team Size
Our client needed a centralized platform to monitor, manage, and analyze their growing portfolio of AI models in production. Existing tools were fragmented, making it difficult to detect performance degradation, track model drift, or understand resource utilization across their ML infrastructure.
We designed and built a comprehensive dashboard that provides real-time visibility into model performance, data quality, and operational metrics. The platform features customizable alert thresholds, automated anomaly detection, and detailed drill-down capabilities for root cause analysis.
The architecture uses a modern event-driven approach with streaming data pipelines, enabling sub-second latency for critical metrics while maintaining cost-efficient batch processing for historical analytics.
Tech Stack
Key Results
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