Deploying NLP Models in Production focuses on moving trained natural language processing models into scalable, real-world environments. It enables organizations to operationalize AI models for tasks like text classification, sentiment analysis, and entity extraction. This training explains core concepts such as model serialization, APIs for inference, containerization, and cloud deployment. It also covers CI/CD pipelines, model versioning, monitoring, logging, and performance optimization techniques. You will learn how enterprises ensure reliability, scalability, and low-latency responses in production NLP systems. It also introduces strategies for handling model drift, data updates, and continuous retraining in live environments. The course highlights best practices for maintaining, updating, and securing deployed NLP models.
