Responsible AI for Developers: Privacy & Safety
This course is part of Responsible AI for Developers.
This course provides developers with practical knowledge and skills to implement responsible AI practices focused on privacy and safety. The curriculum begins with an exploration of AI privacy concepts, covering various techniques for ensuring data protection including de-identification methods and randomization approaches. Students learn to implement differential privacy in machine learning training through DP-SGD (Differentially Private Stochastic Gradient Descent) and federated learning. The course then transitions to AI safety, teaching developers how to evaluate AI systems for potential risks, implement harm prevention strategies, and utilize techniques like instruction fine-tuning and reinforcement learning from human feedback (RLHF) to enhance model safety. Throughout the program, hands-on labs with TensorFlow Privacy and Vertex AI Gemini API provide practical experience in implementing these concepts using Google Cloud products and open-source tools, equipping developers with the technical skills needed to build AI systems that respect user privacy and operate safely.
Instructors:
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What you'll learn
Define key concepts in AI privacy and safety for responsible development
Implement de-identification techniques to protect sensitive information in training data
Apply randomization methods like differential privacy to enhance data protection
Utilize privacy-preserving machine learning approaches including DP-SGD and federated learning
Conduct safety evaluations to identify and mitigate potential AI system risks
Develop strategies for AI harm prevention in various implementation contexts
Skills you'll gain
This course includes:
1.3 Hours PreRecorded video
2 assignments
Access on Mobile, Tablet, Desktop
Batch access
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There are 5 modules in this course
This course provides a comprehensive introduction to AI privacy and safety for developers. The curriculum is structured around two main modules. The first module focuses on AI privacy, exploring methods to protect sensitive information in both training data and machine learning processes. Students learn de-identification and randomization techniques for data preparation, as well as privacy-preserving training approaches like Differentially Private Stochastic Gradient Descent (DP-SGD) and Federated Learning. The module also covers system security implementation in Google Cloud and generative AI contexts. The second module addresses AI safety, teaching evaluation methods for identifying potential risks, harm prevention strategies, and model training techniques that enhance safety such as instruction fine-tuning and Reinforcement Learning from Human Feedback (RLHF). Each module includes hands-on labs where students implement these concepts using TensorFlow Privacy and Vertex AI Gemini API.
Course Introduction
Module 1 · 0 Minutes to complete
AI Privacy
Module 2 · 2 Hours to complete
AI Safety
Module 3 · 2 Hours to complete
Course Summary
Module 4 · 12 Minutes to complete
Course Resources
Module 5 · 40 Minutes to complete
Instructor
Empowering Businesses with Expert Training from Google Cloud
The Google Cloud Training team is tasked with developing, delivering, and evaluating training programs that enable our enterprise customers and partners to effectively utilize our products and solutions. Google Cloud empowers millions of organizations to enhance employee capabilities, improve customer service, and innovate for the future using cutting-edge technology built specifically for the cloud. Our products are designed with a focus on security, reliability, and scalability, covering everything from infrastructure to applications, devices, and hardware. Our dedicated teams are committed to helping customers successfully leverage our technologies to drive their success.
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