MLOps Foundations: Deploying Models with Python, Rust, and MLflow

Transitioning machine learning models from a local notebook to a reliable production environment requires a specialized set of practices. This text-based course guides you through the core principles of Machine Learning Operations (MLOps) to automate and scale your AI workflows. You will transition from writing isolated ML code to designing robust, automated pipelines. You will discover how to leverage Python and Rust for high-performance operations, utilize AI assistants safely, and deploy models across major cloud environments. What you'll learn: - Understand foundational MLOps terminology, system life cycles, and the key differences between traditional software engineering and ML systems. - Build automated training and deployment pipelines using MLflow and cloud machine learning services. - Apply Rust alongside Python to optimize performance-critical data processing and model serving steps. - Configure CI/CD pipelines to automate model testing, validation, and version control. - Deploy large language models (LLMs) and optimize them for production using Hugging Face and ONNX runtimes. - Use AI-assisted development tools responsibly to accelerate your infrastructure-as-code workflows. The course begins with core definitions and architectural patterns before moving into step-by-step written guides on containerization, model registries, and cloud deployment strategies. You will work through practical configuration examples and code snippets designed for real-world application. This course is designed for aspiring MLOps engineers, data scientists, and software developers looking to enter the operations space. No prior DevOps or advanced systems programming experience is required, as we start with the absolute basics. Start reading today to master the engineering practices behind modern production-grade AI.