Monitoring and Maintaining GenAI Systems |Generative AI|

About this course

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 practitioners

Why Should You Take This Course

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