Foundations of AI Engineering and MLOps

Transitioning from writing basic machine learning code to deploying production-ready AI systems requires a solid grasp of both model architecture and operational workflows. This text-based course guides you through the core concepts of AI engineering and MLOps without overwhelming complexity. You will transition from understanding basic model mechanics to structuring, training, and deploying AI models systematically. By reading and working through practical written scenarios, you will develop the mental models needed to manage the entire lifecycle of an AI application. What you'll learn: Understand core neural network architectures, including convolutional and recurrent networks; Apply hyperparameter tuning techniques to optimize model performance and efficiency; Configure basic MLOps pipelines to automate model training and deployment workflows; Explore modern AI concepts, including vector databases and retrieval-augmented generation basics; Implement model monitoring and versioning practices to ensure long-term reliability. The course starts with essential terminology and foundational definitions before moving into neural network structures and optimization. You will then progress to operationalizing these models using modern MLOps principles and deployment strategies. Designed for beginners and aspiring data professionals, this course requires no prior background in AI engineering or DevOps. Start reading today to build a strong foundation in modern AI engineering.