MLOps for Small Language Models with MLflow, GGUF, and ONNX

Stepping into the world of MLOps can seem daunting, especially with the rapid evolution of small language models. This course provides a structured introduction to managing the lifecycle of AI models, focusing on practical tools and formats. By the end of this course, you will understand the core principles of MLOps and gain hands-on knowledge to effectively track, version, and deploy small language models using leading tools and efficient model formats, enabling you to build reproducible and scalable AI applications. What you'll learn: - Understand foundational MLOps concepts, including experiment tracking, model versioning, and reproducibility. - Apply MLflow to manage the entire lifecycle of your machine learning experiments and models. - Learn about the characteristics and applications of small language models, such as Phi-3 mini. - Practice converting and optimizing AI models into efficient formats like GGUF and ONNX for deployment. - Configure basic deployment strategies for compact AI models, ensuring they run effectively in various environments. - Implement best practices for model packaging and artifact management within an MLOps workflow. The course begins with essential MLOps terminology and principles, gradually progressing to practical applications with MLflow. You will then explore specific model types and formats, culminating in strategies for efficient model packaging and deployment. This course is designed for absolute beginners interested in MLOps, machine learning engineers, and data scientists looking to manage and deploy small language models. No prior experience with MLOps or specific tools is required. Start building a robust foundation for managing your AI models today.