|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/monitoring-and-maintaining-genai-systems-generative-ai/
课程评论：没有评论
课程名称：监控与维护生成式人工智能系统 课程概述：全球范围内，人工智能（AI）正在革新各行各业，但其可靠性、安全性与公平性成为主要关注点。本课程全面探讨偏见、安全威胁、监控及合规性等重要问题，同时向学生传授有效运营AI系统所需的基本技能。无论你是AI工程师、数据科学家、MLOps从业者，还是与AI解决方案合作的业务领导者，本课程都将提供对AI系统日志、监控、自动化、安全最佳实践以及负责任AI部署的深入理解。 学习内容： 1. **AI偏见与公平性**：了解生成式AI中偏见的来源，学习识别和修复偏见，确保AI决策的公平性。 2. **AI安全与隐私**：学习常见的AI安全漏洞、模型中毒、恶意攻击及保护AI系统的最佳方法。 3. **MLOps与AI生命周期管理**：学习如何自动化跟踪AI性能、版本控制与回滚方法，以实现强大的AI部署。 4. **伦理与负责任的AI**：了解AI监管框架、透明度与责任，以确保AI实践的伦理与负责任。 5. **AI日志、监控与自动化**：实时使用安全、警报和异常检测工具监控AI性能。 6. **实战案例研究**：分析真实的AI故障、维护问题和提高AI可靠性的有效方法。 适合对象： - 对AI伦理与合规感兴趣的学生与研究人员 - 使用AI进行决策的商务领导者和相关人员 - AI/ML工程师和数据科学家 - IT安全专家和AI治理团队 - MLOps和DevOps从业者 课程亮点： - **全面覆盖**：课程涵盖AI安全、监控、伦理与合规的所有重要部分。 - **应用所学**：通过案例研究，学习可以在AI项目中应用的实际技巧。 - **专家见解**：运用前沿策略以应对AI风险与治理问题。 通过本课程的学习，你将掌握创建、部署与运行安全、公平、透明且有效的AI系统的能力。快来报名，提高你的AI技能吧！
Globally, artificial intelligence (AI) is revolutionising industries, but maintaining its dependability, security, and equity is a major concern. This thorough course addresses important issues including bias, security threats, monitoring, and compliance while giving students the fundamental skills they need to operationalise AI systems effectively.This course will provide you a thorough understanding of AI system logging, monitoring, automation, security best practices, and responsible AI deployment, regardless of whether you are an AI engineer, data scientist, MLOps practitioner, or business leader working with AI solutions.What You Will Learn: AI Bias & Fairness: Discover where bias comes from in Generative AI and learn how to spot and fix biases to make sure AI decisions are fair.AI Security & Privacy: Learn about common AI security holes, model poisoning, hostile attacks, and the best ways to keep AI systems safe.MLOps & AI Lifecycle Management: Learn how to automate tracking AI performance, version control, and rollback methods for a strong AI deployment.Ethical and Responsible AI: Learn about AI regulatory frameworks, transparency, and responsibility to make sure that AI practices are ethical and responsible.AI Logging, Monitoring, and Automation-Use security, alerts, and anomaly detection tools in real time to keep an eye on AI performance.Hands-on Case Studies: Look at real-life AI failures, upkeep problems, and useful ways to make AI more reliable.Who Should Take this Course?Students and researchers interested in AI ethics and compliance.Business leaders and people who use AI to make decisionsAI/ML engineers and data scientistsIT security experts and AI governance teamsMLOps and DevOps practitionersWhy Should You Take This Course?Full Coverage: This lesson goes over all the important parts of AI security, monitoring, ethics, and compliance.Applying What You Learn: Case studies help you learn real-world techniques that you can use in your AI projects.Insights from Experts: Use cutting edge strategies to stay ahead of AI risks and governing problems.You will learn how to create, deploy, and run AI systems that are safe, fair, clear, and effective by the end of this course.Let's Enroll now to improve your AI skills even more!
