This course focuses on the design and implementation of intelligent troubleshooting agents. You will learn to create AI-powered agents that can diagnose and resolve issues autonomously. The course covers natural language processing, decision-making algorithms, and best practices in AI agent development. By the end of this course, you will be able to: 1. Define, describe, and design the architecture of an intelligent troubleshooting agent. 2. Implement natural language processing techniques for user interaction. 3. Develop decision-making algorithms for problem diagnosis and resolution. 4. Optimize and evaluate the performance of AI-based troubleshooting agents. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure and core algorithms and techniques, including approaches using pretrained large-language models (LLMs). Familiarity with statistics is also recommended.
Walkthrough: Creating your code repository Part 1 (Optional)
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Skills You'll Learn
Prompt Engineering, Artificial Intelligence and Machine Learning (AI/ML), Performance Tuning, Natural Language Processing, Generative AI Agents, Test Case, Agentic systems, Debugging, Human Computer Interaction, Machine Learning, Large Language Modeling, Python Programming, Artificial Intelligence
From the lesson
LLM fine-tuning for task-specific adaptation
Taught By
Microsoft