Course Introduction:
This course, titled “LLMOps Instructional Video Series,” is a comprehensive learning program designed to introduce learners to the end-to-end process of Large Language Model Operations (LLMOps) using Microsoft Azure AI Studio. Throughout this 5-part instructional journey, students will explore how to ideate, build, evaluate, and operationalize large language model applications through real-world demonstrations. The course equips participants with practical knowledge to manage the lifecycle of LLMs effectively—covering RAG (Retrieval-Augmented Generation), Prompt Flow, Evaluation Metrics, and Model Monitoring.
It is tailored for AI engineers, data scientists, and developers who wish to gain hands-on experience in modern AI operational practices and optimize LLM applications for real-world production.
Course Presenter:
The course is presented by Takuto Higuchi and Vishnu Pamula, experts from Microsoft’s Data and AI teams. They bring deep expertise in AI development, model optimization, and Azure AI ecosystems. Their engaging, demonstration-based teaching style ensures learners not only understand the theory but also gain practical, real-world skills applicable to enterprise AI solutions.
Course Certificate:
The Qalam Scholar Certificate for this course holds international recognition and is equipped with barcode verification for authenticity. This certificate validates your expertise in LLMOps and enhances your professional credibility, empowering you to pursue advanced roles in AI development, data engineering, or cloud-based AI operations globally.
Learning Objectives:
By the end of this course, students will be able to:
· Understand the foundational concepts of LLMOps and its role in AI lifecycle management.
· Build and augment LLM applications using Azure AI Studio.
· Implement Retrieval-Augmented Generation (RAG) to enhance LLM performance.
· Utilize Prompt Flow for code-first development and model refinement.
· Evaluate LLM flows using built-in metrics for accuracy and reliability.
· Operationalize and monitor LLM applications to ensure consistent performance in production.