Make SQL agents product-ready by learning how to leverage Python, LangChain, and LLM frameworks. First, learn about the overall architecture of SQL AI agents by focusing on building a robust framework that uses deterministic information. Find out how to create effective prompt templates, add context, and inject memory into agents to handle complex and dynamic queries with ease.
Dive into safety assurance, query validations, and safety constraints. Review monitoring systems and error-handling process. Then, set up logs to track the agent performance with MLflow. Last but not least, see how to implement multiple AI agents to process the data.
This course is designed for data scientists and engineers but is also beneficial to data analysts seeking to increase knowledge and build operational skills in AI systems. By the end of the course, you will have learned how to prototype SQL AI agents and deploy them responsibly into production environments.
