MLOps: OpenVino Quantized Pipeline for Grammatical Error Correction

Explore the process of building a Grammar Error Correction (GEC) model using OpenVino quantization techniques in this 55-minute video tutorial. Learn to construct a two-component model featuring a Roberta Base error detector trained with the CoLa Dataset and a Flan-T5 large Grammar correction component fine-tuned with the JFLEG Dataset. Convert both components to IR OpenVino format and quantize the correction component for optimized performance. Gain hands-on experience in implementing CPU inference and access the accompanying notebook for practical application. Enhance your skills in MLOps, data science, and machine learning through this comprehensive demonstration of creating an efficient grammatical error correction pipeline.