MLOps with OpenVINO: Quantized Pipelines for NLP

Deploying language models efficiently requires modern MLOps practices and targeted inference optimization. This text-based guide walks you step by step through creating lightweight, production-ready pipelines for grammatical error correction using OpenVINO and model quantization. You will gain practical skills to compress models, reduce inference latency, and maintain text processing accuracy in real-world environments. What you'll learn: Understand core MLOps principles, model lifecycle concepts, and deployment terminology; Prepare text datasets and configure NLP models for grammar error detection and correction; Convert pre-trained models into optimized representations with OpenVINO tools; Apply post-training quantization techniques to reduce model size without sacrificing quality; Build automated inference pipelines with Python for fast local and cloud execution; Evaluate performance, latency trade-offs, and output quality across benchmark datasets. The course begins with foundational concepts in model compression and MLOps workflows before moving into structured, text-based code exercises and implementation walk-throughs. This course is designed for aspiring machine learning engineers, Python developers, and data practitioners eager to optimize language models for production. Begin reading now to master efficient AI model deployment.