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所在平台: Udemy |
课程主页: https://www.udemy.com/course/non-functional-testing-for-llm-chatbots-and-ai-models/
课程评论：没有评论
课程名称：针对大型语言模型、聊天机器人和人工智能模型的非功能测试 课程概述： 欢迎参加“针对大型语言模型、聊天机器人和人工智能模型的非功能测试”课程。这是一门全面的指南，旨在帮助您掌握测试人工智能系统的基础知识。无论您是开发人员、数据科学家还是人工智能爱好者，课程将为您提供评估、改进并确保人工智能技术的可靠性、性能、安全性和伦理完整性所需的知识和技能。 您将学习到的内容包括： 1. **人工智能测试简介**：理解测试人工智能系统的重要性，涵盖技术性能和伦理考量。学习人工智能故障的潜在影响及负责任的测试如何降低这些风险。 2. **基础模型和大型语言模型的特殊关注**：深入了解测试大型语言模型和基础人工智能系统的独特挑战，这些系统在多个行业推动创新。 3. **人工智能系统评估**：学习如何设计和实施效率测试框架，使用手动和自动工具以提升系统性能和安全性。 4. **对抗性人工智能测试**：理解如何通过对抗性测试技术评估人工智能模型的鲁棒性，检验人工智能系统在面对恶意输入时的稳健性和错误抵抗能力。 5. **伦理和有害性测试的PerspectiveAPI**：学习如何整合PerspectiveAPI等工具来测试人工智能系统的伦理合规性，检测有害或有毒输出，确保人工智能系统遵循安全和伦理标准。 6. **人工智能中的人性**：探索评估人工智能响应中“人性”的概念。学习如何测试人工智能系统是否产生人类相似的、具有上下文意识和共情的输出。 7. **伦理人工智能**：深入了解与人工智能相关的风险及其伦理维度。学习如何测试人工智能系统以确保公正性、透明性以及消除偏见，从而保证遵循负责任的人工智能实践。 8. **使用MLOps的API测试ChatGPT和聊天机器人**：学习如何通过API测试和评估像ChatGPT这样的对话模型，并了解如何将这些测试整合到MLOps管道中以实现持续的人工智能改进。 9. **案例研究**：回顾人工智能测试的真实案例，从行业中的常见陷阱和最佳实践中学习，以确保人工智能的可靠性和安全性。 **适合人群**： 本课程适合希望全面了解测试人工智能系统所需技术和实践的个人。无论您是刚开始人工智能职业生涯、提升专业技能，还是对人工智能系统的可靠性背后的技术和伦理机制感兴趣，此课程都将提供有价值的见解。 立即报名，开始掌握测试人工智能系统的关键技能，以确保您能够为安全、可靠和伦理的人工智能技术发展做出贡献！
Welcome to "Non Functional Testing for LLM, Chatbots and AI Models" your comprehensive guide to mastering the fundamentals of testing AI systems. Whether you're a developer, data scientist, or AI enthusiast, this course will provide you with the knowledge and skills needed to assess, improve, and ensure the reliability, performance, safety, and ethical integrity of AI technologies.What You Will Learn:Introduction to AI Testing: Understand the critical importance of testing AI systems, addressing both technical performance and ethical considerations. Learn about the potential impacts of AI failures and how responsible testing mitigates these risks.Special Focus on Foundation Models and LLMs: Dive deep into the unique challenges of testing large language models and foundational AI systems, which are driving innovation across multiple industries.AI System Evaluations: Learn how to design and implement effective testing frameworks for AI-based systems, utilizing both manual and automated tools to improve system performance and safety.Adversarial AI Testing: Understand how to evaluate the robustness of AI models through adversarial testing techniques, assessing how well AI systems resist manipulation and errors when exposed to malicious inputs.PerspectiveAPI for Ethical and Toxicity Testing: Learn how to integrate the PerspectiveAPI and other tools to test AI systems for ethical compliance and detect harmful or toxic outputs, ensuring AI systems uphold safety and ethical standards.Humanness in AI: Explore the concept of evaluating the "humanness" of AI responses. Learn how to test whether AI systems generate outputs that are human-like, contextually aware, and empathetic in their interactions.Ethical AI: Delve into the risks associated with AI and the ethical dimensions of AI development. Learn how to test AI systems for bias, fairness, and transparency, ensuring adherence to responsible AI practices.Testing ChatGPT and Chatbots Using APIs in MLOps: Learn to test and evaluate conversational models like ChatGPT through APIs, and understand how to integrate these tests into MLOps pipelines for continuous AI improvement.Case Studies: Review real-world examples of AI testing, learning from common pitfalls and best practices used in the field to ensure AI reliability and safety.Who This Course Is For:This course is designed for individuals seeking a comprehensive understanding of the techniques and practices required for testing AI systems. Whether you are starting a career in AI, enhancing your professional skills, or interested in the technical and ethical mechanisms behind AI system reliability, this course offers valuable insights.Enroll now to start mastering the critical skill of testing AI systems, ensuring that you are equipped to contribute to the development of safe, reliable, and ethically sound AI technologies!
