The document outlines the agenda for the 'Build with AI on Google Cloud' series, focusing on Generative AI (GenAI) and related learning paths starting from January 22, 2025. It details the structure of the sessions, including topics like prompt design and responsible AI, alongside a walkthrough of Google's Cloud Skills Boost platform. Participants are encouraged to connect through various GDG chapters across multiple cities and engage within the community for ongoing learning and support.

1. Build with AI
on Google Cloud
Session #1 Intro to GenAI
1/22/2025
Seattle | Surrey | Vancouver | Burnaby

2. GDG Seattle
2
Margaret Maynard-Reid
Yenchi Lin
Clive Boulton
Vishal Pallerla
I/O Extended 2019
2024 Build with AI
DevFest Seattle 2018
DevFest Seattle 2022
WTM Lightning Talks 2018
Cloud Study Jam 2018
DevFest Seattle 2017
DevFest Seattle 2016
DevFest Seattle 2015
DevFest Seattle 2024
Follow GDG Seattle on LinkedIn

3. GDG Surrey
3
Follow GDG Surrey on LinkedIn

4. GDG Vancouver
Follow GDG Seattle on LinkedIn
4
Follow GDG Vancouver on LinkedIn
Join our GDG Vancouver Community
Volunteer Interest Form

5. GDG Burnaby
GDG Burnaby Bevy | LinkedIn
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6. Build with AI
on Google Cloud
Agenda
● Study series overview
● Cloud Skills Boost walkthrough
● CSB beginner path repris
● Intro to GenAI on Google Cloud
● Prompt Design
● Q & A - 20 min
Seattle | Surrey | Vancouver | Burnaby

7. Build with AI
on Google Cloud
Study series
overview
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8. Topics for this session
● Online study series overview
● Intro to GenAI on Google Cloud
● GenAI beginner path
● …
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9. 9
Link to Story on Medium

10. Study series overview
Follow 5 generative AI paths on Google Cloud Skills Boost:
1. 1/22/25 - Beginner: Intro to GenAI (link)
2. 2/5/25 - Generate Smarter GenAI Outputs (link)
3. 2/19/25 - Build & Modernize Apps with GenAI (link)
4. 3/5/25 - Integrate GenAI into Your DataFlow (link)
5. 3/19/25 - Deploy & Manage GenAI Models (link)
Topics are not limited to the above.
Each session: 2 short talks (by Googlers or experts) + Q&A section.
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11. What is a learning path?
A learning path has multiple courses
Each course has videos, recommended reading, quiz & hands-on labs.
You will have at least two weeks to work through the materials
It’s OK if you don’t finish and feel free to study ahead
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12. Access to Cloud Skills Boost
● Sign up here: https://www.cloudskillsboost.google/
● By RSVP, you get free access for a few months
● The videos are accessible by default while labs each require a credit
● You can work on each GenAI paths before or after each session
Note: Make sure to sign up on Google Cloud Skills Boost with the same email that you used
for event RSVP.
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13. Build with AI
on Google Cloud
Cloud Skills Boost
walkthrough
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14. 14
Sign In -Google Cloud Skills Boost

15. 15
Explore Paths

16. Build with AI
on Google Cloud
Beginner:
Intro to GenAI
Learning Path
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17. 5 courses in the learning path
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1. Intro to Generative AI (45 minutes)
2. Intro to LLMs (1 hour)
3. Intro to Responsible AI (30 minutes)
4. Prompt Design in Vertex AI (3 hours 45 minutes)
5. Responsible AI: Applying AI Principals with Google Cloud (2 hours)

18. 1. Intro to generative AI
● AI vs ML vs deep learning
● What is Generative AI? LLMs and generative Image models…
● Supervised vs unsupervised learning
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19. 2. Intro to LLM
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20. 3. Intro to Responsible AI
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21. 4. Prompt Design
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22. 5. Responsible AI: Applying AI Principles with GCloud
1. Be socially beneficial
2. Avoid creating or reinforcing unfair bias
3. Be built and tested for safety
4. Be accountable to people
5. Incorporate privacy design principles
6. Uphold high standards of scientific excellence
7. Be made available for uses that accord with these principles
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23. Build with AI
on Google Cloud
Intro to GenAI
Margaret Maynard-Reid, AI/ML GDE
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24. AI/ML GDE (Google Developer Expert)
3D artist
Fashion Designer
Instructor of MSIS, UW Foster
Ex MS Design Studio, MSR, MS Bing
About me
margaretmz.art
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25. AI, ML, Deep Learning & GenAI
Artificial Intelligence
Machine Learning
Deep Learning
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Generative AI

26. What is Generative AI?
A type of AI that creates new content with generative models:
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Text
Image
Video
Audio
Generative AI
Text
Image
Video
Audio

27. What are Generative Models?
“Generative models: take a machine, observe many samples
from a distribution and generate more samples from that
same distribution”.
- Ian Goodfellow 2016
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28. What is
an LLM?
LLMs Explained
[...] [...] [...]
[...]
0.02
0.03
0.9 0.01 0.0 …
Dogs Rain Drops Fish Wind …
and
cats
raining
It’s

29. Type of Generative Models
● 2014 Generative Adversarial
Networks (GANs)
● 2016 Autoregressive Models
● 2019 Variational autoencoders
(VAEs)
● Flow-based models
● 2020 Diffusion models
● 2022 Diffusion Transformer
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Source: Lilian Weng blog (link)

30. Diffusion Models
1. Gradually add gaussian
noise to training data
2. Learn how to reverse the
process to generate
images from noise.
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Source: Nvidia developer blog (link)
Forward image diffusion
Generative reverse denoise

31. CLIP: Contrastive Language-Image Pre-training
CLIP is a bridge between NLP and computer
vision, connecting text and Images
It has a text encoder and image encoder,
trained with 400 million image-text pairs.
● DALLE, DALLE-2
● Stable Diffusion
● Imagen, Imagen 2, Imagen 3
Paper: Learning Transferable Visual Models From
Natural Language Supervision
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32. Diffusion Transformer
Paper: Scalable Diffusion Models with
Transformers
SoTA models using diffusion
transformer:
● Pixart-a
● SORA
● Stable Diffusion 3
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33. The U-Net
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U-Net architecture (image source: U-Net paper)
2015 paper for medical
imaging segmentation
Used in many popular GenAI
models:
● Pix2Pix
● CycleGAN
● Diffusion Models (DDPM)
● DALL-E
● Midjourney
● Stable Diffusion…

34. U-Net vs Diffusion Transformer
U-Net
- not crucial to the good
performance of the
diffusion model
- struggle with capturing
long-range dependencies and
global context in the input
data
Diffusion Transformer
- More flexible
- Can use more training data
and larger model parameters
- Transformers can model
long-range dependencies
without the need for deep
networks or large filters,
because of the
self-attention mechanisms
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35. Timeline: generative AI in vision
Source: Sora paper
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Imagen 3
Veo/VideoFX

36. Thank you!
Connect with me learn more about AI, art & design!
@margaretmz
@margaretmz
@margaretmz
@margaretmz
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37. Build with AI
on Google Cloud Prompt Design - By Priti Y.
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What is Prompt Design?

38. Prompt Design Workflow
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Define Objectives and Expected
Outcomes
Create the Prompt Content, Structure,
Components
Test the
Prompt
Identify Areas for Improvement
Refine
Iterate
Iterate

39. The Components of a Prompt
● Define the Task
● Contextual Information
● Include Few-Shot Examples
● System Instructions
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40. Common Pitfalls in Prompt Design
● Vague Instructions
● Not Providing Enough Context
● Poorly Structured Prompts
● Not Using Examples
● Not Assigning a Role or Persona
● Not Breaking Down Complex Tasks
● Not Specifying the Output Format
● Not Iterating on Prompts
● Not Experimenting with Parameters
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41. Best Practices for Designing Prompts
● Assign a Role
● Structure Prompts
● Instruct the Model to Explain its Reasoning
● Break Down Complex Tasks
● Experiment with Parameter Values
● Specify the Output Format
● Optimize Image Prompts
● Tailor for Task Type
● Consider the Model’s Limitations
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42. Demo
Prompt Design in Vertex AI
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43. Thank you!
Let's connect to explore AI Projects/Research and Board Games together!
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@pritiyadav888
@mentors/priti-yadav

44. Build with AI
on Google Cloud
Q&A
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45. More questions?
Post them on GDG Surrey
Discord server #gen_ai_gcp
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Scan Me

46. Have fun studying!
Action items:
● Join discord - post your questions there
● Get access to Cloud Skills Boost credits
● Complete Beginner GenAI path on CSB
● Get started on the 2nd GenAI path on CSB
Next session:
● Feb 5, 2025 - Session #2 - Deep Dive (RSVP)
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