MLOps with MLflow: Fine-Tuning Flan-T5 for Seq2Seq Tasks

Transitioning machine learning prototypes into structured, manageable workflows requires reliable tracking and fine-tuning practices. This text-based course guides you through fine-tuning transformer models like Flan-T5 for sequence-to-sequence classification while managing experiments seamlessly with MLflow. You will read clear explanations and study practical code patterns to prepare text datasets, configure training parameters, monitor evaluation metrics, and organize your model artifacts. What you'll learn: - Understand foundational MLOps concepts and sequence-to-sequence NLP architectures - Prepare and tokenize text datasets for language model training - Fine-tune Flan-T5 models for text classification and transformation tasks - Track parameters, metrics, and runs using MLflow tracking tools - Manage model versioning, packaging, and artifacts in the MLflow Model Registry - Apply evaluation strategies and best practices for reproducible ML workflows The course begins with fundamental definitions, core MLOps terminology, and basic model concepts before guiding you step-by-step through dataset formatting, fine-tuning scripts, and lifecycle management. This course is created for developers, data analysts, and beginners in machine learning engineering who want a practical foundation in NLP fine-tuning and experiment tracking. No prior experience with MLflow or advanced deep learning is required. Start reading to build reliable, trackable machine learning pipelines with confidence.