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Manual data management processes requiring custom scripts caused delays in ML model training. Inefficient ML workflows with time-consuming kernel recompilation and infrastructure management slowed product updates. Poor monitoring systems led to service disruptions and suboptimal MTBF/MTTR metrics.
Software solutions provider for retail and finishing materials/ décor brands, specializing in AI-driven tools for interior design and product visualization
Data specialists to reduce operational hours by 60% through automation. ML engineers to cut model update times by eliminating manual infrastructure tasks. System reliability improvements to reduce downtime by 75% and accelerate time-to-market for AI features. Enhanced cross-team collaboration between business and technical units.