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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.
Mlops
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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%.