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Responsible AI for Developers: Privacy & Safety

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Overview

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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.

Syllabus

  • Course Introduction
    • Course Introduction
  • AI Privacy
    • 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
    • Module 1: Quiz
  • AI Safety
    • 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
    • Module 2: Quiz
  • Course Summary
    • Course Summary
    • Reading
  • Course Resources
    • Course Resources
  • Your Next Steps
    • Claim credential

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