Democratizing AI Scalable ML with BigQuery ML & Vertex AI

1. Brand book

2. Democratizing AI:
Scalable ML with
BigQuery ML & Vertex AI
CEO, AdFlex
GDE @ Cloud (since 2016)
Tu Pham
Phuong

3. Google Cloud Next Proprietary
Your AI needs to
be fueled by your
business data
Your business
data needs to be
unlocked by AI

4. Google Cloud Next Proprietary
Agenda
01 Barriers in Industry
02 Data to AI Journey
03 Built in AI/ML in BigQuery
04 BQML Demo
Sentiment Analysis
Analyzing Images
04 Questions

5. Google Cloud Next Proprietary
Data and AI Trends
71%
Of organizations
plan to use
databases
integrated with
gen AI
capabilities
Of organizations
are satisfied with
their legacy data
platform’s
support for AI,
indicating there
is a lot of room
for improvement
14%
Data and AI Trends Report 2024 Data and AI Trends Report 2024

6. Google Cloud Next Proprietary
Businesses struggle: Meeting the
needs of the AI era
84%
Time and
complexity from
data to insights is
long due to
fragmented tools
not designed to talk
to each other
business pain point
is the need to train
and upskill
employees in the
use of data and AI
#1
Data and AI Trends Report 2024 MIT Technology Review Insights

7. Google Cloud Next Proprietary
The Challenge: AI/ML's
Perceived Barriers
Complexity Expertise Cost
'AI/ML is too
complex for our
team.’
'We lack specialized
data science skills.'
'Infrastructure and
resources are too
expensive.'
Accessibility
'Finding the right
tools is a challenge.’

8. Google Cloud Next Proprietary
Unified Platform for Data to AI
Simplifying and
unifying data
analytics & ML
around BigQuery
BigQuery
Unified Platform from Data to AI
Unified experience
Single product UX
New GenAI powered
experiences
Collaborative workflows
Unified data Unified engines
Structured / unstructured
Iceberg / Delta / Hudi
GCS
Cross-cloud (AWS, Azure)
Unified governance
SQL
Spark
Python
Remote functions
Business intelligence

9. Google Cloud Next Proprietary
Typical ML workflow poses
many challenges
Issues:
Streaming/batch data
Data processing Export data
Manage infra?
Learn a new
language?
Data
Governance?
Deploy ML
model
Train ML model
(e.g. Python)
Where do I
host?
Multiple products & roles can lead
to unnecessary complexity & costs
BigQuery
(data warehouse)

10. Google Cloud Next Proprietary
BigQuery ML brings AI/ML
capabilities to your data platform
Issues:
Streaming/batch data
Data processing Export data
Manage infra?
Data
Governance?
Deploy ML model
Train ML model
(e.g. Python)
Where do I
host?
Simplify with BigQuery ML - data and
ML workloads in one place
BigQuery
(data warehouse)

11. Google Cloud Next Proprietary
Built-in AI/ML | BQML
All
workloads
Why BigQuery?
VS
➢ Require every ML use case to go
through more specialized systems
that require advanced skill sets
➢ Provide ML access to
more users through a
simple SQL interface
Machine Learning for all Built-in ML with
SQL
➢ Execute, iterate, and automate ML initiatives all within
BigQuery using predefined models
➢ Leverage external models developed in Tensorflow
directly from SQL
➢ Export developed models for use in Vertex AI

12. Google Cloud Next Proprietary
Full suite of ML platform capabilities
Feature
engineering
Inference
Train model
BigQuery
Tables or
Object tables
★ Rich built in ML
featurization functions
e.g. bucketize, encode,
normalize, resize image
★ Feature transformation
modules
★ Generate embeddings
★ Feature store
integration
★ Training managed by
BQ in BQ infra and in
VertexAI
★ Choose from 18 built
in models covering
major use cases
★ HPO support
★ Batch inference via SQL
★ Import model files (tf,
xgboost, onnx) for inference
★ Remote inference via Vertex
MaaS or user managed
endpoints
★ Streaming inference with
continuous queries
Tune, evaluate,
register models
★ Model monitoring
★ Model evaluation for
LLMs and predictive
ML models
★ Register to Vertex
Model Registry
Monitoring

13. Google Cloud Next Proprietary
GenAI using BigQuery ML
Vertex AI
GCP’s AI Platform
BigQuery ML
SQL interface to Vertex
AI Gemini, 3P and OSS
models
BigQuery
Cloud Data Warehouse
Model as a Service (via Vertex AI)
1P
Pro, Flash
3P Open Models
Hugging Face models deployed via Model
Garden can be used in BigQuery ML
Via User managed
Vertex AI endpoints
User Managed endpoints

14. Google Cloud Next Proprietary
Generative AI use cases
in BigQuery
Entity
extraction
From Text: “Extract
product name and
size from the
following text”
From Images:
“Output parts of the
car with damage in
this picture”
Data
enrichment
“Which country is this
city in”
Sentiment
analysis
“Give me the
sentiment of the
online reviews in
column x”
Content
generation
“Create a customized
marketing email
based on the
customer information
in BQ table “
Video analysis
“Summarize the
content of the video”
● Bring your imagination to your prompts!
● Enforce schema for structured data output
● Batch inference LLM calls are 50% cheaper (vs online inference)
Gemini Model Pricing

15. Google Cloud Next Proprietary

16. Google Cloud Next Proprietary
Hands On Lab (Task 1-6)
● Sentiment Analysis
● Extract themes from reviews
● Generate Response to Customer Reviews

17. Google Cloud Next Proprietary
Demo 1

18. Google Cloud Next Proprietary
Hands On Lab (Task 7)
● Analyze Images
● Generate Response after analyzing
the images

19. Google Cloud Next Proprietary
Demo 2

20. Brand book | Layouts and templates
Layouts and templates
Go to Slido.com
Code:
gfs-aibootcamp
For questions

21. Layouts and templates
Brand book | Layouts and templates
Thank you!
Any questions?