The document discusses the role of AI and Automated Machine Learning (AutoML) in transforming various industries, emphasizing their relevance in the modern and rapidly changing landscape. It highlights H2O.ai's contributions, especially during the COVID-19 pandemic, and outlines their platforms that enhance data science capabilities while providing practical examples. Additionally, the document sheds light on the operational advantages of using AutoML, such as improved efficiency, the ability to augment data science teams, and the importance of explainability in AI solutions.

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James Orton | Australia and New Zealand | Data Scientist
AI and AutoML:
Debunking Myths
2020

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AI and AutoML: Debunking Myths
Are they overhyped or the answer to our problems?
Beyond the hyperbole, what are autoML and AI?
How are they helpful, and when are they not?
Why are they more relevant and valuable than ever?
Our world is changing rapidly, many organisations will need to adapt quickly.
AI and AutoML are not magic but it can be transformative, find out how today!
Get practical tips and see AutoML in action with a real world example. We’ll
demonstrate how AutoML can augment your Data Scientists, supercharging your
team and giving your organisation the AI edge in record time.

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James Orton
Data Scientist @ H2O.ai
Australia and New Zealand
Connect with me
https://www.linkedin.com/in/jamesortonthedataman/
james.orton@h2o.ai
… and who are H2O.ai?
Who is that talking?

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Founded in Silicon Valley 2012
Funding: Series D
Investors: Goldman Sachs, Ping An,
Wells Fargo, NVIDIA, Nexus Ventures
We are Established
We Make World-class AI Platforms
We are Global
H2O Open Source Machine Learning
H2O Driverless AI: Automatic Machine Learning
H2O Q: AI platform for business users
Mountain View, NYC, London, Paris, Ottawa,
Prague, Chennai, Singapore, Melbourne
220+ 1K
20K 180K
Universities
Companies Using
H2O Open Source
Meetup Members
Experts
H2O.ai Snapshot
We are Passionate about Customers
Commonwealth Bank Australia, IP Australia,
Customer Service NSW, Aetna/CVS, Allergan,
AT&T, CapitalOne, Citi, Coca Cola, Bredesco, Dish,
Disney, Franklin Templeton, Genentech, Kaiser
Permanente, Lego, Merck, Pepsi, Reckitt Benckiser

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Gartner 2020: H2O.ai is a Visionary in Two MQs
A new MQ and
the only AI
platform
company in the
quadrant.
2020 Cloud AI for Developer
Services MQ
2020 Data Science and Machine
Learning MQ
Named a Visionary,
with the strongest
“Completeness of
Vision” in the entire
quadrant.
Strengths:
1. Automation
2. Explainability
3. High-Performance ML Components
Strengths:
1. Automation
2. Ease of Use and Explainability
3. Excellent Customer Support

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But what is AI?

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What is Artificial Intelligence: Use cases, H2O.ai works on
with our customers:
Save Time. Save Money. Gain a Competitive Edge.
Wholesale / Commercial
Banking
• Know Your Customers (KYC)
• Anti-Money Laundering (AML)
Card / Payments Business
• Transaction frauds
• Collusion fraud
• Real-time targeting
• Credit risk scoring
• In-context promotion
Retail Banking
• Deposit fraud
• Customer churn prediction
• Auto-loan
Financial Services
• Early cancer detection
• Product recommendations
• Personalized prescription
matching
• Medical claim fraud detection
• Flu season prediction
• Drug discovery
• ER and hospital
management
• Remote patient monitoring
• Medical test predictions
Healthcare and
Life Science
• Predictive maintenance
• Avoidable truck-rolls
• Customer churn prediction
• Improved customer viewing
experience
• Master data management
• In-context promotions
• Intelligent ad placements
• Personalized program
recommendations
Telecom
• Funnel predictions
• Personalized ads
• Credit scoring
• Fraud detection
• Next best offer
• Next best action
• Customer segmentation
• Customer churn
• Customer recommendations
• Ad predictions and fraud
Marketing and Retail

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Examples of the impact of AI Transformations
…real-time individualized experience
…dynamic yield optimizationBreak then fix
…personalized quality of serviceCustomer service silos
…personalized healthcareMass treatment
…real-time trade surveillanceDaily risk analysis
Mass branding
WITH AIPRE-AI
AI allows
organizations to
shift interactions
from…
Reactive
Post Transaction
Proactive
Pre Decision

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How H2O.ai is Contributing to COVID-19
Expertise
H2O.ai’s data science experts
are contributing their
knowledge to solve pressing
problems with the pandemic
AI Platforms
H2O.ai is contributing its
Driverless AI and Q platform
to model, predict, and
visualize data sets
Sri Ambati
CEO and Founder, H2O.ai
1. Hospital staffing predictions
2. ICU transfers and triage
3. Population risk segmentation
4. Predicting the spread of COVID-19.
5. Predicting operational efficiency and
resilience during a pandemic
6. Hospital supply chain predictions
7. Predicting responses by city,
hospitals
8. Sepsis predictions
Problems we are solving
“
Data Sets
H2O.ai is evaluating global
and open health data sets to
determine patterns
“Data Science can save
lives today. AI is an
incredible force to do
good for humanity.”
AI Solutions
H2O.ai is creating pandemic
and health specific solutions
for general use

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What is AutoML?
Automated machine learning (AutoML) is the process
of automating the process of applying machine learning to
real-world problems. AutoML covers the complete pipeline
from the raw dataset to the deployable machine learning
model. AutoML was proposed as an artificial intelligence-
based solution to the ever-growing challenge of applying
machine learning.

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Prepare Data
ML Algorithms
Models
New Data
Prediction
Deployed Model
Explanation
Explanation
Features
(Original + Engineered)
Hyperparameters
AI App
End User
ML AlgorithmsML Algorithms
ModelsModels
Deployed
Model
Typical ML Workflow
Model
Explanation
Model
Report
Model
Management &
Monitoring
Model
Engineering
Tuning
(Scorer)
Training Data Explore Data

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Not all AutoML is created equal

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So we know what AI, ML and AutoML are…
We can see that AI and ML are already having a big
impact across many sectors of the economy
… but why use AutoML?

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Why now?
Lets address
the elephant in
the room, or
why we are all
in separate
rooms?

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After Covid-19 the World Will
Never Be the Same. But Maybe, It
Can Be Better!
1
5
Confidentia
l
Vanessa Bates Ramirez,
SingularityHub

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Why now?
The model you built last year
to identify credit risk probably
is not relevant anymore
The world is changing at a
fast pace
How do you rapidly iterate,
build and deploy ML to keep
up?

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Does not replace data scientists
Look for a solution that augments your current capability
Often can not do end to end data preparation
But it can tackle components very well, speak to us about auto data augmentation
Does not replace domain knowledge
Look for a solution that allows you to inject your own business specific knowledge
Does not, by itself, create an AI culture or data literacy
But it can create space for this
AutoML: What are some of the cautions?

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Who is on the team?
Business leader, data
scientists, IT professional
Determine the problems you
want to solve with metrics (time,
money, # of customers, etc)
Determine where you have
data, need data, and can
use technology to find
answers and predictions.
Find answers efficiently.
Learn from others in the
data science community
Ask the Right Questions
Data & Technology
Community
Create a Data Culture
Understand and explain the
models. Use leading edge
technologies to guard for
bias, explain a model, and
present this to regulators
Trust in AI
2
1
3
4
5
5 Keys
to unlock your AI

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A Very Simplistic AI project
Framing
• Culture and
Community
• The right
questions
Technical
Execution
• Do ML!
Impact
• Building
Trust
• Deployment

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Manual ML
Framing
Technical Execution
Impact

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Auto ML
Framing
Technical
Execution
Impact

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Let’s see H2O.ai AutoML in action

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• Automatic feature engineering,
machine learning and interpretability
• Fully automated machine learning
from ingest to deployment
• User licenses on a per seat basis
annually
• GUI-based interface for end-to-end
data science
• A new and innovated
platform to make your own
AI apps
• Enterprise commercial
software
• Easy and intuitive platform to
have AI answer your
question
H2O.ai: AI Platforms
In-memory, distributed
machine learning algorithms with
H2O Flow GUI
Open Source H2O Driverless AI H2O Q
• 100% open source – Apache
V2 Licensed
• Integration with Apache Spark
• Enterprise support subscriptions
• Interface using R, Python on
H2O Flow
H2O Model Ops
• AI deployment platform built
for DevOps and MLOps
• Scalable to support high
throughput and low latency
model scoring environments
• Comprehensive model
monitoring
• Drift Detection and retrain
ModelOps

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The AI
Advantage
Using H2O
Driverless AI
Talent
Enterprise-ready
Get answers faster
Augment existing teams
• Develop AI models faster
• Saving Time
• Deploy production-ready models in hours vs. months
• New models with better accuracy
• Benchmarking existing models for better accuracy
• Allow data scientists of any level of expertise to develop
production-ready ML models
• Target more use cases
• Answer to increasing business demand to predict outcomes
• Get results across the business
Cross-enterprise efforts to scale AI
Enhance productivity
Improve models

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Driverless AI
Features Targe
t
Data Quality and
Transformation
Modeling
Table
Model
Building
Model
Data Integration
+
Driverless AI:
Automates Data Science and ML Workflows
Highly Iterative Process

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Challenges in AI Model Development
Basic Encoding
Feature Generation
Advanced Encoding
Feature Engineering
Algorithm Selection
Parameter Tuning
Model Building
Model Ensembles
Pipeline Generation
Model Explainabilty
Model Deployment
Model Documentation
• Time consuming
• Requires advanced
skill set
• Creating new feature
combinations requires
advanced skill
• Time consuming
• Requires advanced
knowledge of
algorithms and
parameters
• Creating ensembles
is an advanced skill
• Time consuming
• Requires different set of skills to
deploy models
• Explaining how models make
decisions is critical to building
trust with business stakeholders
and regulators
The entire process is highly iterative and can take weeks or months to develop a single
production-ready model.

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H2O.ai sets an example in providing rich
explainability functionality, using diverse
techniques such as K-LIME, LIME-SUP,
Shapley, variable importance, decision tree
surrogate, ICE, partial dependence plots,
disparate impact analysis and “what-if
analysis.” The AutoDoc capability
automatically generates a complete set of
explanations in document format - Gartner
2020
Trust and Understanding of AI
Invited presentations: JSM (‘18, ‘19),
KDD (‘19); Accepted paper: NeurIPS
(‘19)

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Catalog.h2o.ai (140+ BYOR Custom Recipes)

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Automatic AI and ML
in a single platform
AI to do AI
Delivers insights
and interpretability
Customize and extend with
130+ open source recipes or
your domain expertise
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Driverless AI: The Platform to Make Your Own AI

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GET
STARTED
TODAY
• Learn about what healthcare, life sciences, finance and
insurance customers are doing with H2O.ai at our
website: https://www.h2o.ai/solutions/
• Take Driverless AI for a 21-day trial or
2 hour Free Cloud Test Drive and tutorials
• Meet the Makers at an event or meetup near you
• Watch a webinar to learn more
• Follow us on LinkedIn or Twitter @h2oai

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Thank you

33. Questions?