Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017

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This talk will walk through the important building blocks of Automated AI. Rajiv will highlight the current gaps in the analytics organizations, how to close those gaps using automated AI. Some of the issues discussed around automated AI are the accuracy of models, tradeoffs around control when using automation, interpretability of models, and integration with other tools. These issues will be highlighted with examples of automated analytics in different industries. The talk will end with some examples of how automated AI in the hands of data scientists and business analysts is transforming analytic teams and organizations.

Data & Analytics

AI AUTOMATION
Enabling the AI-Driven Enterprise
Rajiv Shah
DataRobot

AI Importance
Artisan AI
Automated AI
Demo

AI industry will grow to $70 billion by 2020
from just $8.2 billion in 2013, according
to IDC research.

“If your competitor is rushing to build AI and
you don't, it will crush you.”
-Elon Musk

Enabling the AI-Driven Enterprise
Where AI is applied in every business process to predict outcomes.
The AI-Driven Enterprise adapts to new conditions at incredible
speeds and continually self-optimizes based on predicting the future.

THE OPPORTUNITY FOR MACHINE LEARNING IN ANY BUSINESS
Banking
Insurance
Healthcare
Media
Pharma
Telco
Retail
Government
Energy
Transportation
Marketing Sales Risk
Human Resources Logistics
Predicting customer Lifetime Value (LTV)
Churn
Customer segmentation
Product mix (best product mix to reduce churn)
Cross selling/recommendation algorithms
Up selling
Channel optimization
Discount targeting
Responses rates
Reactivation likelihood
Adwords optimization and ad buying
In store traffic patterns
Aircraft scheduling
Lead prioritization
Demand forecasting
Pricing
Market Basket
Inventory management / Dynamic Pricing
Promos/upgrades/offers
Resume screening
Employee churn
Training recommendation
Talent management
Credit Risk
Fraud detection
Accounts Payable Recovery
Anti-money laundering
Insurance Claims prediction
Readmission Risk
Warranty Analytics
Claim Prediction
Procurement
Warehousing
Cost Analysis
Product life cycle
Demand Forecast
Assembly
Turnover

Everyone is taking advantage of
AI, the question is how fast can
you go

http://www.eyeonsouthflorida.com/wp-content/uploads/2016/08/artisan3.jpg

THE AI BOTTLENECK: DATA SCIENTISTS
Data
Scientist
Math &
Stats
Domain
Expertise
Data
Scientist
Programming
Skills
Knowledge of the business
Knowledge of the data
Ability to write code to gather data
Ability to write code to explore/inspect data
Ability to write code to manipulate data
Ability to write code to extract actionable intel
Ability to write code to build models
Ability to write code to implement models
Foundational statistics
Internals of algorithms
Practical knowledge and experience
Knowing how to interpret and explain models
PREREQUISITES

Accelerate the process of researching, testing, and deploying predictive algorithms. Enable more
people to help research, test, and deploy predictive algorithms.
Unmet demand for
Data Science
THE PROBLEM
KEY
Demand for predictive models
Supply of data scientists

There are not enough data
scientists (and it's not changing
anytime soon)

AUTOMATED MACHINE LEARNING: THE NEW PREREQUISITES
Data
Scientist
Math &
Stats
Programming
Skills
Knowledge of the business
Knowledge of the data
PREREQUISITESDomain
Expertise

Automated AI will fill
the gap of data scientists

http://farm3.static.flickr.com/2315/5702627963_36f5015261.jpg

Create data science superheroes
with Automated AI

Automated AI is about lots of
relevant options - - - not the
dogma of AI elitists

An AI solution should utilize
existing open source AI
algorithms

How?
partial dependence
feature importance
Why?
Reason codes

No Black Boxes!
Understand a model at the
prediction level

Build & deploy should be as
similar as possible

AI AUTOMATION
Enabling the AI-Driven Enterprise
Rajiv Shah
rajiv.shah@datarobot.com
rajcs4

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Hadoop adoption is a journey. Depending on the business the process can take weeks, months, or even years. Hadoop is a transformative technology so the challenges have less to do with the technology and more to do with how a company adapts itself to a new way of thinking about data. There are challenges for companies who have lived with an application driven business for the last two decades to suddenly become data driven. Companies need to begin thinking less in terms of single, silo’d servers and more about “the cluster”. The concept of the cluster becomes the center of data gravity drawing all the applications to it. Companies, especially the IT organizations, embark on a process of understanding how to maintain and operationalize this environment and provide the data lake as a service to the businesses. They must empower the business by providing the resources for the use cases which drive both renovation and innovation. IT needs to adopt new technologies and new methodologies which enable the solutions. This is not technology for technology sake. Hadoop is a data platform servicing and enabling all facets of an organization. Building out and expanding this platform is the ongoing journey as word gets out to businesses that they can have any data they want and any time. Success is what drives the journey. The length of the journey varies from company to company. Sometimes the challenges are based on the size of the company but many times the challenges are based on the difficulty of unseating established IT processes companies have adopted without forethought for the past two decades. Companies must navigate through the noise. Sifting through the noise to find those solutions which bring real value takes time. As the platform matures and becomes mainstream, more and more companies are finding it easier to adopt Hadoop. Hundreds of companies have already taken many steps; hundreds more have already taken the first step. As the wave of successful Hadoop adoption continues, more and more companies will see the value in starting the journey and paving the way for others.

The Big Data Journey – How Companies Adopt Hadoop - StampedeCon 2016

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