Build with AI on Google Cloud Session #1

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

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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

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GDG Surrey 3 Follow GDG Surrey on LinkedIn

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

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GDG Burnaby GDG Burnaby Bevy | LinkedIn 5

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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

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Build with AI on Google Cloud Study series overview 7

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Topics for this session ● Online study series overview ● Intro to GenAI on Google Cloud ● GenAI beginner path ● … 8

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9 Link to Story on Medium

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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. 10

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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 11

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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. 12

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Build with AI on Google Cloud Cloud Skills Boost walkthrough 13

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14 Sign In -Google Cloud Skills Boost

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15 Explore Paths

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Build with AI on Google Cloud Beginner: Intro to GenAI Learning Path 16

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5 courses in the learning path 17 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)

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1. Intro to generative AI ● AI vs ML vs deep learning ● What is Generative AI? LLMs and generative Image models… ● Supervised vs unsupervised learning 18

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2. Intro to LLM 19

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3. Intro to Responsible AI 20

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4. Prompt Design 21

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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 22

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Build with AI on Google Cloud Intro to GenAI Margaret Maynard-Reid, AI/ML GDE 23

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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 24

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AI, ML, Deep Learning & GenAI Artificial Intelligence Machine Learning Deep Learning 25 25 Generative AI

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

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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 27

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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

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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 29 Source: Lilian Weng blog (link)

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

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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 31

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Diffusion Transformer Paper: Scalable Diffusion Models with Transformers SoTA models using diffusion transformer: ● Pixart-a ● SORA ● Stable Diffusion 3 32

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The U-Net 33 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…

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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 34

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Timeline: generative AI in vision Source: Sora paper 35 Imagen 3 Veo/VideoFX

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Thank you! Connect with me learn more about AI, art & design! @margaretmz @margaretmz @margaretmz @margaretmz 36

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Build with AI on Google Cloud Prompt Design - By Priti Y. 37 What is Prompt Design?

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

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The Components of a Prompt ● Define the Task ● Contextual Information ● Include Few-Shot Examples ● System Instructions 39

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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 40

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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 41

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Demo Prompt Design in Vertex AI 42

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Thank you! Let's connect to explore AI Projects/Research and Board Games together! 43 @pritiyadav888 @mentors/priti-yadav

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Build with AI on Google Cloud Q&A 44

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More questions? Post them on GDG Surrey Discord server #gen_ai_gcp 45 Scan Me

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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) 46