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Building real-time intent recognition achieving sub-500ms latency with 70% accuracy improvement, and implementing Query AutoComplete using Ghosting approach with N-gram models and fine-tuned GPT-2.
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How I designed and deployed an automated MLOps system that reduced ML cycle time by 90%, managing end-to-end model lifecycles including training, versioning, serving, and drift detection.- Published on
Reduced weekly construction site reports time by 70% using ML models (YOLOv8, UNet, SAM, Transformers) for materials detection. Built event-driven integration handling 1000+ concurrent jobs.- Published on
System design exploration on detecting harmful content using NLP, Computer Vision, and ML at scale. Covers sentiment analysis, hate speech detection, content filtering, and real-time moderation strategies.- Published on
Improving edge inference speed by 25-30% via quantization (QAT, TFLite, TensorRT, OpenVINO) and pruning. Building DeepOps for real-time edge device management reducing model management time by 80%.