This module introduces the course structure and objectives.

Course Introduction

This module focuses on AI Privacy. It provides various techniques and tools to help achieve privacy in data and machine learning training.

Overview of AI Privacy

Privacy in Training Data: De-identification techniques

Privacy in Training Data: Randomization techniques

Privacy in Machine Learning Training: DP-SGD

Privacy in Machine Learning Training: Federated Learning

System Security on Google Cloud

System Security on Gen AI

Lab: Differential Privacy in Machine Learning with TensorFlow Privacy

Differential Privacy in Machine Learning with TensorFlow Privacy

AI Privacy Question 1

AI Privacy Question 2

AI Privacy Question 3

AI Privacy

This module focuses on AI Safety. It provides various techniques and tools to help achieve safety in AI.

Overview of AI Safety

Safety Evaluation

Harms Prevention

Model Traing for Safety: Instruction Fine-tuning

Model Traing for Safety: RLHF

Safety in Google Cloud GenAI

Lab: Safeguarding with Vertex AI Gemini API

Safeguarding with Agent Platform Gemini API

AI Safety Question 1

AI Safety Question 2

AI Safety Question 3

AI Safety

This module provides a summary of the entire course by covering the most important concepts, tools, and technologies.

Course Summary

Reading

Course Resources

This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.

Responsible AI for Developers: Privacy & Safety

It explores practical methods and tools to implement AI privacy and safety recommended practices.

Differential Privacy in Machine Learning with TensorFlow Privacy

Responsible AI for Developers: Privacy & Safety

Instrucțiuni pentru exercițiu