Becoming a Data-Driven Organization with Machine Learning

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Becoming a Data Driven Organization

with Machine Learning By Peter Harrington



Goals for this talk

• Introduce Machine Learning (ML)

• Talk about how we can take ML outside of the

• Share Some experience from the trenches

• Simplicity

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Agenda

• Introduce Myself

• Define Data Driven and ML

• Common tasks in ML

• Sample of some ML algos with examples

• *Interpretable ML

• *ML & Agile Development

• *What is a “Data Scientist”?

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About me

• Author of Machine Learning in Action

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My employer

• Provide Customers with a list of WHO is using WHAT

product.

• Customers are willing to pay us for this data.

• Collect data from numerous document sources where

companies are talking about themselves.

• Our product is data

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What we do

• Natural Language Processing

• Knowledge Graph of Business Information

• 1.5B documents

• Update results daily

• Try to keep infrastructure costs down

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Spam

• We use lots and lots of Java

• We are hiring

• Santa Barbra, California

• Sunnyvale, California

• Apply on our website: www.hgdata.com

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“Data Driven”

• “Data driven means that progress in an activity is

compelled by data, rather than by intuition or personal

experience.” --Wikipedia

• This talk is going to show how you can use some

techniques from Machine Learning to help make data

driven decisions in your work place, or help your

applications make decisions.

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What is Machine Learning?

• Some tools to allow a machine to learn from data.

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Example

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Example (continued)

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What is Machine Learning?

• Some tools to allow a machine to learn from data.

• Tools that can make decisions from non-deterministic

data

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Common Tasks in Machine Learning

• Supervised Learning

• Predicting a numerical value, this is called regression.

• Predicting categories, (spam or not spam for example) this is

called classification

• Unsupervised Learning

• Clustering

• Association rule mining {men, diapers} {beer}

• Topic modeling

• Semi-Supervised Learning

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Where is ML being used today?

• Face detection

• Handwriting detection in mail

• Voice recognition (Siri, Sync)

• Answering questions (IBM Watson, Google)

• Forecasting weather

• Stock Trading

• Recommending things when you shop

• Spam detection online: email, forums

• Law: forecasting results, extracting info from docs

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Where else?

• Spacecraft

• Self driving cars

• Identifying whales

• Predicting strokes

• Fighting financial fraud

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Why is ML useful?

• You do not need to be a domain expert to make a

prediction/forecast.

• Example 1: mathematician out predicts a Law professor at

Supreme court rulings

• Example 2: an economist out predicts wine snobs at predicting

the best vintages.

Both of the above examples are from a book called:“Super Crunchers” by

Ian Ayres

• We are not trying to escape study of these fields, rather

we are often asked to study fields that few have studied

before.

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Classification Example

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Decision Tree Example

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Data wrangling

• The data doesn’t always come as easily as in these toy

examples.

• 50-90% of our time is spent getting the data into the

system

• Reasons why we need to do this

• Wrong format

• Not being recorded

• Political reasons

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Regression Example

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Ridge Regression Example

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Semi-Supervised Learning

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Image taken from: http://bioinformatics.oxfordjournals.org/content/24/6/783/F1.expansion.html



How can you do this?

• Collect Some data

• Put the data into some existing package in your

language of choice.

• Take the resulting model and put it into your application.

Understand the model.

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ML code Data Model

H = 0.9*W +1 2.4



Better Approach

• Introduce Machine Learning (ML)

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ML code Data Model

H = 0.9*W +1 2.4

Training

Set

Test

Set Test Code

1.5 lbs Error:



Agile Development and ML

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Traditional

Development

Agile

Development

Agile Research

Requirements Negotiate with

customer

Working prototypes

to constantly refine

requirements

Establish target

accuracy,

Sufficient Data

Implementation Comprehensive plan Small teams, quick

implementation

cycle

Rapid research

cycle: focus on

data or algorithm

improvements

Measurement Validate that the

software meets specs

with tests

Iterate with

customer to

evaluate if

requirements are

met

Accuracy metrics

dominate,

Headroom analysis

used to guide next

sprint



Interpretable ML

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Interpretable ML

• “Black box” models are not easy to interpret, and may

be poorly received.

• Some models like decision trees are easy to interpret

but may have not have the best performance.

• Check out: Decision Lists and Sparse Integer Lists if this

is something you are interested in.

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What is a Data Scientist?

A. A cross between a statistician and a developer?

B. A developer who knows ML?

C. A buzzword we use to attract developers?

D. All of the above?

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Thanks again for coming!

• Questions?

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True AI vs. Modern AI

• True AI seeks to understand how the human brain works

by creating an artificial version.

• Modern AI is a collection of hard problems.

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Becoming a Data-Driven Organization with Machine Learning

Software

Transcript of Becoming a Data-Driven Organization with Machine Learning