Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Using Generative AI on GCP: 2023 I/O Extended M...

Using Generative AI on GCP: 2023 I/O Extended Mbarara

This session will introduce the audience to generative AI and how it enables us to efficiently make faster decisions, thereby saving cost and time.

It will also introduce the audience to Vertex AI which is the GCP service for generative AI. The session will also explain the end-to-end process of ML-Ops solutions on Google Cloud. The session will end with sample use cases and companies that have leveraged Vertex AI features in serving their customers.

Jekayin-Oluwa Olabemiwo

June 24, 2023
Tweet

More Decks by Jekayin-Oluwa Olabemiwo

Other Decks in Technology

Transcript

  1. GOOGLE IO Extended - SPEAKER TEMPLATE Table of Contents 04

    06 12 14 17 19 20 21 24 26 Introduction What is AI? Gen AI How Gen AI Works Transformers Application of Gen AI Building Gen AI on GCP Benefits & Use cases Quick Demo Recommendation
  2. About this Session o Introductory guide on gen AI o

    Good for all levels o Familiarity with computers and the internet required Expected Outcomes o Know what gen AI is and its benefits o Understand how gen AI relates to the AM/ML ecosystem o Know the GCP services available for building gen AI o Understand how to build with Vertex AI
  3. A kind of AI technology that can produce a variety

    of content. o Text o Images o Audio It could also produce synthetic data What is Generative AI Page 5
  4. Page 6 A field of CS that deals with making

    systems that could learn, reason and act autonomously. What is AI?
  5. Page 7 A subfield of AI involves training models to

    recognize patterns in input data and then make predictions or helpful predictions from new data (previously unseen) drawn from the data used to train the model. o Takes input data o Draws new (never seen before) data from the input data o Makes decisions based on the new data What is ML?
  6. Requires labelled input data for the training to happen. Humans

    would labelled the data and pass them to the model for training. Yes labels, yes tags Yes input variables No output variables Examples: Classification i.e identifying data Takes in raw unlabelled input data. It then finds the patterns in the dataset or group the data into groups based on similarities. No labels, no tags Yes input variables Yes output variables Examples: clustering and grouping similar data points Supervised Unsupervised Kinds of ML Models Page 8
  7. Page 9 o A subset of ML based on neural

    networks o It trains the model to process data like the human brain What is DL?
  8. Discriminates between different kinds of data instances Trained on a

    labelled dataset Used for classification or prediction Conditional probability, P(A|B) Example: the model takes in a dataset of cat and dog images and it’s able to classify dogs and cats Generates new data instances Generates new data similar to the one it was trained on Used to generate data Joint probability, P(A,B) Example: the model takes in a dataset of cat images and it is able to generate a new cat image. Discriminative Generative Kinds of DL Models Page 10
  9. 13 Generative AI Page 13 Don’t lose track: AI >

    ML > DL > Gen AI o A subset of Deep Learning o Learns patterns from input data to develop new and unique output o Even realistic content at the human intelligence level o Can create new content and ideas Photos, music, video
  10. Learns patterns from input data to develop new and unique

    output Creates a statistical model by learning from the input data Learns pattern Creates model It uses the statistical model to predict expected response to prompts and generates the appropriate content Generate content Gen AI: How it works Page 14
  11. ✓ Images ✓ Audio ✓ Text/Speech ❌ Number ❌ Class

    ❌ Probability Page 15 Emphasis on Content Generation
  12. Transformers ushered in a massive revolution in the NLP ecosystem.

    A transformer consists of an encoder which encodes the input and a decoder that decodes the representation for the appropriate task. Transformers 2017 Introduced in NLP Page 19 Output I’m alright. Thank you! Encoder Decoder Transformer Model Generative Pre-trained Transformer Model input Hello, how are you?
  13. o Code generation o Voice assistants o Personalized learning o

    CAD Design in Manufacturing o And more Application of Gen AI Page 20
  14. o Generative AI Studio: robust platform for building o Generative

    AI App Builder: no-code platform o Duet AI o Palm API/Makersuite o Vertex AI: model garden of foundation models trained on large amounts of data Building Gen AI on GCP Page 21
  15. o Improved efficiency o Personalized and Customized solutions for businesses

    & individuals o Better customer experience o Reduced costs o Enhanced creativity Benefits of Gen AI Page 21
  16. o Payroll and HR management systems o Using Gen AI

    to provide powerful business insights for better leadership decision-making Page 23
  17. o Drive-thru food ordering fast food chain o Building automated

    food ordering experience with Google Cloud Gen AI Page 24
  18. A Demo with Generative AI Studio in Vertex AI Page

    25 console.cloud.google.com/vertex-ai/generative
  19. o Discover Generative AI: https://ai.google/discover/generativeai/ o Generative AI learning path:

    https://www.cloudskillsboost.google/journeys/118 Recommendation Page 26