

December 2025 monthly summary for Quant_RUC: Delivered a Batch Inference Notebook for Sentiment Analysis with Ollama, enabling batch processing of customer reviews and prompt-driven responses. The notebook provides a reproducible workflow, prompts for sentiment classification, and local LLM inference with Ollama. This work reduces manual processing time, accelerates customer feedback analytics, and sets the stage for scalable model experimentation across the product analytics domain. Commits associated with this feature include 28cc436e8f1ad2e4f37014c57abcefc53ad231b4 and 3ee6d4a9df8ee6e08289b7c72752ad0f85c583dc ("update codes").
December 2025 monthly summary for Quant_RUC: Delivered a Batch Inference Notebook for Sentiment Analysis with Ollama, enabling batch processing of customer reviews and prompt-driven responses. The notebook provides a reproducible workflow, prompts for sentiment classification, and local LLM inference with Ollama. This work reduces manual processing time, accelerates customer feedback analytics, and sets the stage for scalable model experimentation across the product analytics domain. Commits associated with this feature include 28cc436e8f1ad2e4f37014c57abcefc53ad231b4 and 3ee6d4a9df8ee6e08289b7c72752ad0f85c583dc ("update codes").
September 2025: Delivered a cohesive set of enhancements across Quant_RUC, focusing on multilingual educational materials, resource consolidation, and scalable automation. The work improves accessibility for learners, strengthens the learning resources, and enables efficient candidate communications.
September 2025: Delivered a cohesive set of enhancements across Quant_RUC, focusing on multilingual educational materials, resource consolidation, and scalable automation. The work improves accessibility for learners, strengthens the learning resources, and enables efficient candidate communications.
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