Introduction to Responsible AI in Practice

Length 1 day

Course overview

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Why study this course

The development of AI has created new opportunities to improve the lives of people around the world, from business to healthcare to education. It has also raised new questions about the best way to build fairness, interpretability, privacy, and safety into these systems.

In this course, you will do a high-level exploration of Google's recommended best practices for responsible AI usage across different areas of focus: Fairness, Interpretability, Privacy and Safety. Along the way, you will learn how you can leverage different open-source tools and tools on Vertex AI to explore these concepts and spend time considering the different challenges that arise with generative AI.

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What you’ll learn

This course teaches participants the following skills:

Overview of Responsible AI principles and practices
Implement processes to check for unfair biases within machine learning models
Explore techniques to interpret the behavior of machine learning models in a human-understandable manner
Create processes that enforce the privacy of sensitive data in machine learning applications
Understand techniques to ensure safety for generative AI-powered applications

Google Cloud at Lumify Work

Lumify Work is Australia's only national Google Cloud Authorised Training Partner. Get the skills needed to build, test, and deploy applications on this highly scalable infrastructure. Engineered to handle the most data-intensive work you can throw at it, Lumify Work can support you through training wherever you are in your Cloud adoption journey.

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Who is the course for?

This course is intended for the following participants:

Machine learning practitioners and AI application developers wanting to leverage generative AI in a responsible manner.

Course subjects

Module 1: AI Principles and Responsible AI

Google's AI Principles
Responsible AI practices
General best practices

Module 2: Fairness in AI

Overview of Fairness in AI
Examples of tools to study fairness of datasets and models
Lab: Using TensorFlow Data Validation and TensorFlow Model Analysis to Ensure Fairness

Module 3: Interpretability of AI

Overview of Interpretability in AI
Metric selection
Taxonomy of explainability in ML Models
Examples of tools to study interpretability
Lab: Learning Interpretability Tool for Text Summarisation

Module 4: Privacy in ML

Overview of Privacy in ML
Data security
Model security
Security for Generative AI on Google Cloud

Module 5: AI Safety

Overview of AI Safety
Adversarial testing
Safety in Gen AI Studio
Lab: Responsible AI with Gen AI Studio

Prerequisites

To get the most out of this course, participants should have:

Familiarity with basic concepts of machine learning
Familiarity with basic concepts of generative AI on Google Cloud in Vertex AI

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.