GDG Boise - Innovating with Google Cloud Artificial Intelligence

This presentation explores how organizations can leverage Google Cloud's AI and machine learning tools to transform business processes. It covers AI fundamentals, Google Cloud solutions like BigQuery ML and AutoML, and provides key takeaways for continued learning. Designed for developers and tech enthusiasts, it offers insights to grow in AI and build the future of businesses.

1. Innovating with Google Cloud
Artificial Intelligence
GDG Boise
Boise

2. New to Cloud Computing
"I'm just starting to learn about cloud computing."
New to Artificial Intelligence
"I'm new to the world of artificial intelligence."

Who is this for?

3. Goals
1.AI/ML Fundamental Concepts: The Basics of Intelligence
2.The Google Cloud AI Pathways: Choosing Your Tool
3.Immediate Impact: Vertex AI & Custom Solutions
4.Action: What You Should Do Next
Agenda

4. Boise
GDG Boise DevFest 2025: Planning for December

5. Artificial Intelligence

6. Machine Learning

7. AI and ML Relationship

8. ●Historical Data Use: Dashboards and reports analyze past
performance to calculate metrics and identify trends.
●Limitation: Historical data is backward-looking and doesn’t
guide future decisions.
●AI/ML Role: Enables predictive analytics for forward-looking,
data-driven decisions.
●ML Function: Learns from data patterns to make proactive
predictions.
Data Analytics & Business Intelligence

9. Problems ML Solves

10. ML Data Quality

11. ML Model
Inference
The process of using a
trained machine
learning model to make
predictions or decisions
based on new, unseen
data.
ML Model Training
The process of
teaching a machine
learning model to make
accurate predictions or
decisions by using a
dataset.
Definitions

12. Tenets of Responsible AI
●Be socially beneficial.
●Avoid creating or reinforcing unfair bias.
●Be built and tested for safety.
●Be accountable to people.
●Incorporate privacy design principles.
●Uphold high standards of scientific excellence.

13. - Me
Sometimes you just
need to take a break…
[and that’s ok]

14. ML options on Google Cloud

15. BigQuery ML

16. BigQuery ML

17. Who is BigQuery ML for?
●Data analysts who want to build ML
models without Python or Java
●Business users looking to use
predictive analytics with familiar SQL
tools
●Organizations aiming to streamline
ML workflows and integrate with
Vertex AI for deployment

18. Pre-trained APIs

19. ●Organizations without data scientists
seeking fast, low-effort ML solutions
●Developers building smart apps
needing image, text, or speech
capabilities quickly
●Flexible deployment across
on-premises, private cloud, or
Google's public cloud
Who is Pre-trained APIs for?

20. Pre-trained ML Models APIs

21. AutoML

22. ●Designed for non-experts to build
custom ML models
●No coding required for tasks like
image classification and natural
language processing.
Who is AutoML for?

23. Vertex AI
Vertex AI unifies Google Cloud ML tools
for training and deployment

No coding via GUI
AutoML lets users build ML models
with no coding via GUI


Custom Models
Ideal for businesses needing custom
models without deep ML expertise


AutoML: Simple yet powerful.
Google Cloud APIs
Integrates with Google Cloud APIs for
advanced applications


Extensible
Supports tasks like image recognition,
NLP, and text analysis

24. Custom Training

25. Vertex AI: Custom ML for Maximum Impact

26. Key Features
End-to-end ML workflow
support: from data collection
to model deployment and
monitoring
Custom model building with
pre-built algorithms and tools
for training, labeling, and
prediction
Who It’s For
Data scientists and engineers
building custom ML models
Businesses seeking maximum
differentiation and innovation
through tailored AI solutions

Why Use Vertex AI
Enables fully customized
models tailored to specific
business needs
Offers flexibility with no
one-size-fits-all approach —
tools adapt to your use case

Vertex AI

27. Key Features
●End-to-end ML platform
●Flexible tools and
libraries
●Open source and widely
used
●Integrated with TPUs for
speed
Who It’s For
TensorFlow is for researchers
and developers building and
deploying machine learning
models. It’s a software library
for ML.

Why Use TensorFlow
It accelerates ML workloads up
to 200x faster with TPUs,
reducing training time from 26
hours to 7.9 minutes. Open
source and community-driven, it
supports both research and
production.

TensorFlow

28. Scenario Two
I’m a marketing analyst and I
want to predict customer
churn based on our BigQuery
data warehouse using only
SQL. Which path?
Scenario One
I’m a web developer building a
moderation tool and I need to
check images for explicit
content. Which path?

Scenario Three
I’m an ML Engineer building a
cutting-edge proprietary
trading algorithm. Which
path?

Which ML Pathway?

29. Google Cloud AI Solutions: Beyond Customization

30. Document AI
Extracts and classifies data
from unstructured
documents (invoices,
receipts, etc.) for analysis.
Contact Center AI
Enhances customer
interactions, improves
agent support, and
personalized care.

Discovery AI for Retail
Uses machine learning to
optimize product displays,
improving relevance and
sales.

Fully built AI solutions
Cloud Talent Solution
Extracts and classifies data
from unstructured
documents (invoices,
receipts, etc.) for analysis.

31. Final Take: Google AI and ML Solutions

32. What to Consider When Adopting AI & ML

33. Questions &
Answers

35. Boise
GDG Boise DevFest 2025: Planning for December

36. Artificial intelligence (AI) and Machine Learning (ML) are
transforming industries by enabling organizations to manage, analyze,
and act on growing data volumes. However, handling this data can be
laborious, requiring new tools and methodologies.
Innovating with Google Cloud Artificial Intelligence, explores key AI
and ML concepts and how they can drive business value. It also
introduces Google Cloud's AI and ML solutions, including pre-trained
APIs, AutoML, and custom tools, to help transform your business.
Overview