MLOps Fundamentals: Fine-Tuning Flan-T5 for Text Classification

Developing and deploying AI models, especially large language models (LLMs), requires more than just coding; it demands a systematic approach to management and deployment known as MLOps. This course introduces you to the essential practices that bridge the gap between AI development and production. By the end of this course, you will understand the core concepts of MLOps and gain practical experience using MLFlow to fine-tune the Flan-T5 model for real-world text classification tasks, preparing you to confidently manage the lifecycle of your AI projects. What you'll learn: * Understand the foundational concepts of MLOps and its role in the machine learning lifecycle. * Learn to set up and utilize MLFlow for experiment tracking, model versioning, and model registry. * Practice fine-tuning the Flan-T5 large language model for specialized text classification tasks. * Apply effective strategies for data preparation and evaluation specific to LLM fine-tuning. * Configure basic deployment considerations for fine-tuned models within an MLOps framework. * Implement foundational CI/CD principles for continuous model integration and delivery. This course begins by defining key MLOps terminology and principles, progresses to hands-on MLFlow implementation, and culminates in the practical fine-tuning of Flan-T5 for text classification, covering the entire workflow from data to deployment considerations. This course is designed for beginners in MLOps and machine learning, with no prior experience in MLOps, MLFlow, or advanced LLM concepts required. Begin your journey into practical MLOps and AI model management today.