In this talk I discuss six "worst practices" in Artificial Intelligence, so that you don't make the same mistakes as you embark on your AI and Machine Learning journey! The full talk (in cantonese) is here: https://youtu.be/NIIztmpA6Hc?t=1172

1. William Tsoi
Customer Engineer,
Google Cloud

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Worst Practices
In Artificial Intelligence
October 6 2021

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“More than 87% of data
science projects never
make it into production”
- Multiple studies and surveys -
Source: https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/

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Worst Practice:
Solutions looking for a
problem
01

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Google Cloud
has lots of ML
solutions

6. Content management & archives
Vision and Video Intelligence
“...that's where Google Cloud Vision came in. The image
analysis results were high-quality, the pay-as-you-go pricing
model enabled us to get something to market quickly without
an upfront cost (aside from engineering resources), and we
trusted that the service backed by Google expertise could
seamlessly scale to support our needs.”
Ben Kus
Senior Director of Product Management
Bringing high quality image recognition
and OCR to cloud content management
Content classification
Image & video search
Industrial inspection
Quickly deploy
highest quality AI
Sight Language Conversation Struct Data

7. Translation and Natural Language
“Transparency, speed, and global access are critical to our
clients. Google Cloud Translation helps us share the widest
possible view of our global market.”
Adela Quinones
Product Manager
Connecting users to important news
from sources in over 40 languages.
Entity extraction
Document classification
Sentiment analysis
Localization
Quickly deploy
highest quality AI
Sight Language Conversation Struct Data

8. Video transcription
Speech, voice, and conversational bots
"Once our team began working with Dialogflow, we were able
to move so quickly that we met or exceeded every milestone
or goal… We were so efficient because Dialogflow was easy to
train people on, and easy to use."
Mandi Galluch
Digital Experience Program Leader
Simplifying customer interactions with
conversational technology
Quickly deploy
highest quality AI
Audible content development
Contact Center
Voice commands
Sight Language Conversation Struct Data

9. Forecasting
Predictive analytics
Structured Data
“The speed, precision, and scale of AutoML Tables allowed us
at FOX SPORTS to create an entirely new experience for
millions of cricket fans across Australia. By training our model
on historical cricket match data, we could predict when wickets
would fall 5 minutes before it happened on the pitch... ”
Christopher Pocock
Marketing Director
Creating the cricket fan experience of
the future with live predictions
Quickly deploy
highest quality AI
Personalization
Portfolio optimization
Sight Language Conversation Struct Data

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Start with the
problem, not
with the
solution

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Place Image Here
Setting the right objective
● ML Model objectives should
match business objectives
● Example: For a Product
Recommendation model, what
should you be optimizing for?
○ Click-through rate?
○ Conversation rate?
○ Revenue $?

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Google Cloud’s
Enterprise AI
Strategy

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Worst Practice:
Not adopting the ML Mindset
02

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Machine Learning changes the way you think
about a problem. The focus shifts from a
mathematical science to a natural science,
running experiments and using statistics, not
logic, to analyse its results.”
Peter Norvig
Research Director, Google

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Ensure that you (and your
stakeholders) are prepared for some
uncertainty.
(expectation management is just as important as
technical ability)
Your model may not produce the
right result on the first try

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Place Image Here
Never manage a
Machine Learning
project in a waterfall
way
William Tsoi
Customer Engineer, Google

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Worst Practice:
Solving the wrong type of
problem with AI
03

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● Recommending different content to
different users
● Prediction of future events
● Personalization that improves UX
● Natural language understanding
● Image recognition
● Anomaly detection
● Conversational bots
When is AI suitable?

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● Maintaining predictability
● Minimizing costly errors
● Complete transparency
● Optimizing for high speed & low cost
● Automating high value tasks
When is AI not suitable?

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Worst Practice:
AI without a solid data
foundation
04

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Closing the Data Value Gap
DATA VALUE
68% of companies are unable to realize tangible
& measurable Value from Data.
175 ZB exp. in 5 Years
10X in last 8 Years
2/3 of Data Produced
is NEVER Analyzed

22. The big big data decision:
Data warehouse or data lake?
Use case
characteristics
Understanding your business
Data Warehouse
(TB scale)
Answer “known” questions
Access “known” data
Structured data
SQL access and manipulation
Data Lake
(PB scale)
Answer “unknown” questions
Access “unknown” data
Unstructured (raw) and structured data
Code-involved access and exploration
Exploring your business
Data type
and access

23. Google’s Smart Analytics Platform powered by BigQuery
Analyse and process data with any
tool or persona to enable fast,
broad-based analysis of data
Pub/Sub
(Messaging)
Dataflow
(Streaming)
AI Platform
Dataproc
(Spark)
BigQuery
Kafka
DTS Connector
Services
Data Catalog
Data Fusion
(Code-free ETL)
SQL and BI Tools
Democratised
Services
Data QnA, Connected Sheets
Data Lakes
Databases
Discover, manage, and secure data
across varied stores and locations to
break silos and deliver complete analysis
External Public
Clouds
DLP
Security Controls
Ingest any volume of data from any
source in real time through native
connectors and streaming capabilities
Enhancing the capabilities of the Enterprise Data Warehouse

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Worst Practice:
Finishing once the model is
developed
04

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Developing the model
is just the beginning...
Modeling Code

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…a product requires so much more
Configuration
Data Collection
Data
Verification
Feature Extraction Process Management
Tools
Analysis Tools
Machine
Resource
Management
Serving
Infrastructure
Monitoring
ML Code

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TFX is seamlessly integrated with GCP's full suite of ML tools
Container Registry
Artifact Store
Cloud Storage
Scalable Inference
AI Platform Prediction
Processing
Cloud Dataflow
Serverless Training
AI Platform Training
Data warehouse
BigQuery
Extract Data
Prepare
Data
Train
Model
Validate
Data
Vertex Pipelines
Evaluate
Model
Validate
Model
Deploy
Model
(TFX)

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Worst Practice:
Ignoring bias and ethics
05

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Model meets the following three criteria:
● Model exhibits systemic bias
● Bias affects traditionally
disadvantaged groups
● Bias results in harm
How do we define unfairness?

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Our solution
Explainable AI

32. Thank you