MLOps with MLflow and spaCy: Training NER Models

Building reliable natural language processing systems requires more than just training models; it demands structured tracking, reproducibility, and lifecycle management. This text-based course guides you through creating custom Named Entity Recognition (NER) models using spaCy and managing your machine learning experiments seamlessly with MLflow. You will gain the practical skills needed to prepare text datasets, configure spaCy pipelines, log parameters and metrics, and register your models for production-ready MLOps workflows. What you'll learn: - Understand foundational NLP concepts and core MLOps terminology - Prepare and annotate text datasets for custom Named Entity Recognition tasks - Configure and train custom NER pipelines using spaCy - Track experiments, hyperparameter runs, and evaluation metrics using MLflow - Manage model artifacts, versions, and deployment states with the MLflow Model Registry - Apply best practices for reproducible and maintainable NLP workflows The course begins with foundational definitions and setup instructions before progressing step-by-step through dataset preparation, pipeline training, and experiment tracking through clear written explanations and practical code walkthroughs. This course is designed for aspiring data scientists, machine learning engineers, and developers looking for a clear, accessible entry point into NLP and MLOps. No prior experience with MLflow or spaCy is required. Start reading today to build robust, trackable natural language processing pipelines.