A New Year in Data Science: ML Unpaused

A New Year in Data Science:  
ML Unpaused
Data Day Texas 
Austin, 2015-01-10
Paco Nathan, @pacoid

Observations about Machine Learning, Data Science,
Big Data, Open Source, Cluster Computing, Notebooks,
etc., over the past year … plus, a look ahead

Backstory: The Sun Also Rises
Some wake early in
the morning and go
build buildings

Some gaze into the
heavens, sit back,
and explain the
process…

Some gaze into the
heavens, sit back,
and explain the
process…

Clearly, provably,  
our Sun revolves
around the Earth  
at an observable
rate

Others create and
evaluate models to
predict the Earth’s
orbit of the Sun

Sometimes, when  
the sky gods become
angry and obscure
the Sun as our due
punishment…

We grow scared and
react: sacriﬁces must
be offered, our plans
must change, etc.

Sometimes, when
the sky gods become
angry and obscure
the Sun
punishment…

We grow scared and
react: sacriﬁces must
be offered, our plans
must
These points are what  
I’d like to discuss today

Whither Data Science?
twitter.com/josh_wills/status/198093512149958656

Feel free to disagree, but I find that definition  
to be flawed…

to be ﬂawed…

1. That ignores DevOps (how’s that working out?)  
and Visualization/Design (ditto)

to be ﬂawed…

1. That ignores DevOps (how’s that working out?)  
and Visualization/Design (ditto)

2. When the CEO asks you to help explain why  
revenue nose-dived over the past month…
neither ﬁeld has a clue about how to model
business phenomena

Software Engineering:  
implement and test a model that somebody selected

…almost ignores the matter of modeling entirely,  
at least not since old school types like Dijkstra

!
Statistics:  
measure and justify a model that somebody selected

…was never particularly good at teaching how to  
model problems – as two renowned statisticians,  
William Cleveland and Leo Breiman, noted

Software Engineering:
implement and test a model that somebody selected
…almost ignores the matter of modeling entirely,
at least not since old school types like
!
Statistics:
measure and justify a model that somebody selected
…was never particularly good at teaching how to
model problems – as two renowned statisticians,
William Cleveland
Both ﬁelds are necessary,
but not sufﬁcient

TheThorn in the Side of Big Data: too few artists 
Christopher Ré, Stanford 
safaribooksonline.com/library/view/strata-conference-santa/9781491900321/
part92.html

TheThorn in the Side of Big Data: too few artists
Christopher Ré, Stanford
safaribooksonline.com/library/view/strata-conference-santa/9781491900321/
part92.html
“You should think
about features and
not algorithms”

Floyd Marinescu observed about the aftermath  
of EJBs in Brief History…

Intended for building framework components, 
e.g., for IBM, Oracle, Sun, but not many others

Based on RMI, prior to notions  
like RESTful web services
Enterprise Java Beans: Lessons from hate-watch reality television

Maybe a handful of people in the world would  
ever actually need to use EJBs, but those few
people wanted a spec

Then, for tragic political reasons (MSFT envy),  
Sun Microsystems made EJBs prominent in  
their Java APIs

Fortunately, we evolved: Spring, JBoss, etc.,  
those came along as relatively more sane tech

Now we see the Docker thing soar, with notions
such as microservices displacing legacy cruft

(BTW, if you haven’t yet, check out Weave)

I mention this because, to me, EJB represented  
a convoluted form of template thinking:
developing complex web apps  
for the sake of  
developing complex web apps

IRL developers and template thinking don’t
determine public policy… right?

To paraphrase Dean Wampler, consider WordCount
a simple apps written for MapReduce in Hadoop …
~50 lines of unapologetic Java that feels hella like
writing EJBs:

Compare that with functional programming, where  
the same WC app is three lines of easily-read Scala
when run in Apache Spark:

Check out Dean’s talk at 11:00,  
“Why Scala isTaking Over  
the Big DataWorld”
Compare that with functional programming, where  
the same WC app is three lines of easily-read Scala
when run in Apache Spark:

Hadoop suffers because, IMHO, that convoluted  
EJB style of developer-centric template thinking
staged a coup
Perhaps we could
“donate” some
OSS talent…

Send a pull
request…

Or something.

Lies, Damn Lies,  
Statistics, and  
Data Science

Probability got going, formally, in the 16th c. –  
although interesting mathematical estimations  
trace back to classical times

Arabs in the 9th c. used frequency analysis –  
later rediscovered by Europeans during the  
early Italian Renaissance

Statistics followed, originally more about what  
we might call demographics – through 18th c.
Lies, Damn Lies, Statistics, Data Science

Laplace, Gauss, et al., bridged the ﬁelds in the  
late 18th c. using distributions (what we studied  
in Stats 101) to infer the probability of errors  
in estimates

!
!
Much of the 19th/20th c. work was about using
goodness of ﬁt tests, etc., justifying some distribution

• generally speaking, that require samples

• that, in turn, implies batch windows

That kind of template thinking in action 
really lurvs it some batch windows

While 19th/20th c. stats work focused on
defensibility

21st c. work, w.r.t. Big Data apps, focuses more  
on predictability – plus there’s a shift in how we
make estimates…
BTW, doesn’t it seem weird to crunch through piles
of data in large batch jobs, at large expense, when
the results get used to approximate features
ultimately? Why not perform that in stream?

A fascinating, relatively new area pioneered by
relatively few people – e.g., Philippe Flajolet

Provides approximation with error bounds
using much less resources (RAM, CPU, etc.)
highlyscalable.wordpress.com/2012/05/01/
probabilistic-structures-web-analytics-
data-mining/

algorithm use case example
Bloom Filter set membership code
MinHash

set similarity code
HyperLogLog set cardinality code
Count-Min Sketch frequency summaries code
DSQ streaming quantiles code
SkipList ordered sequence search code

E.g., ±4% could buy you two orders of magnitude
reduction in the required memory footprint for  
an analytics app

!
OSS projects such as Algebird and BlinkDB
provide for this newer approach to the math of
approximations at scale

E.g., ±4% could buy you two orders of magnitude
reduction in the required memory footprint for
an analytics app

!
OSS projects such as
provide for this newer approach to the math of
approximati
Oscar Boykin at 14:00,  
“Aggregators: Modeling  
Data Queries Functionally”

co-author of Algebird, Scalding

Data Science is inherently interdisciplinary

To paraphrase Chris Ré, emphasis on algorithms  
is relatively minor in the grand scheme –

Especially when compared to needs for modeling
business problems effectively

To wit: beyond phenomenology, leading  
into quantitative analysis and repeatable results

On the one hand, CS + Stats do not quite address
those needs…
The Interzone

On the other hand, Physics
does well to teach modeling –

I like to hire physicists to work
on Data teams…
The Interzone
They tend to get the interdisciplinary aspects:  
got the math background, coding experience,
generally good at systems engineering, etc.

Not saying we should all rush out to get Physics
degrees; there’s something to be learned there,  
vital for the work and priorities ahead

I mention this because we are at a crossroads,  
which has more to do with the physical world –  
some talks here at DDTx15 help illustrate that

Vast implications for Health Care, Transportation,
Agriculture, Energy, Gov, Manufacturing in general…

More about that  
in a bit –
The Interzone

Most of the ML libraries that one encounters  
today focus on two general kinds of solutions:

• convex optimization

• matrix factorization

The Libraries: Alexandria Redux

One might think of the convex optimization  
in this case as a kind of curve ﬁtting – generally  
with some regularization term to avoid overﬁtting,  
which is not good
Good Bad

For supervised learning, used to create classifiers:

1. categorize the expected data into N classes

2. split a sample of the data into train/test sets

3. use learners to optimize classifiers based on 
the training set, to label the data into N classes

4. evaluate the classifiers against the test set,
measuring error in predicted vs. expected labels

Bokay, great for security problems with simply
two classes: good guys vs. bad guys

How do you decide what the classes are  
for more complex problems in business?

That’s where the matrix factorization
parts come in handy…

For unsupervised learning, which is often used  
to reduce dimension:

1. create a covariance matrix of the data

2. solve for the eigenvectors and eigenvalues  
of the matrix

3. select the top N eigenvectors, based on
diminishing returns for how they explain
variance in the data

4. those eigenvectors deﬁne your N classes

An excellent overview of ML deﬁnitions  
(up to this point) is given in:
To wit:  

Generalization = Representation + Optimization + Evaluation
A Few UsefulThings to Know about Machine Learning 
Pedro Domingos 
CACM 55:10 (Oct 2012) 
http://dl.acm.org/citation.cfm?id=2347755

evaluationoptimizationrepresentationcirca 2010
ETL into
cluster/cloud
data
data
visualize,
reporting
Data
Prep
Features
Learners,
Parameters
Unsupervised
Learning
Explore
train set
test set
models
Evaluate
Optimize
Scoring
production
data
use
cases
data pipelines
actionable results
decisions, feedback
bar developers
foo algorithms
Algorithms and developer-centric template thinking
only go so far in a workﬂow…
Results are shown in blue, and the real work  
is highlighted in red

ETL into
cluster/cloud
data
data
visualize,
reporting
Data
Prep
Features
Learners,
Parameters
Unsupervised
Learning
Explore
train set
test set
models
Evaluate
Optimize
Scoring
production
data
use
cases
data pipelines
actionable results
decisions, feedback
bar developers
foo algorithms
only go so far
Results are shown in
is highlighted in
1. focus on features not algorithms

2. learn how to model business
problems by leveraging data

3. notice the workﬂows needed?

4. leave the dev-centric thinking  
for odd city council meetings

ETL into
cluster/cloud
data
data
visualize,
reporting
Data
Prep
Features
Learners,
Parameters
Unsupervised
Learning
Explore
train set
test set
models
Evaluate
Optimize
Scoring
production
data
use
cases
data pipelines
actionable results
decisions, feedback
bar developers
foo algorithms
only go so far
Results are shown in
is highlighted in
Matthew Kirk 12:00 
“Lessons Learned: Machine Learning
andTechnical Debt”
Ted Dunning 13:00 
“Computing with Chaos”
Julia Evans 15:00 
“Data Pipelines.They're a lot of work!”
Christopher Johnson 16:00 
“Scala Data Pipelines for Music
Recommendations”

Even so, business demands exceed far beyond
what classiﬁers and labels alone can give us…

Businesses lurv Optimization, gobs of it; in  
that context ML libraries today merely scratch
the surface

Round hole, square peg

Imagine that you compete with FedEx… how do
you optimize delivery routes for airplanes, trucks,
trains, nanodrones, hoverboards, etc.?
Which do you optimize: fuel cost,
delivery time, maintenance schedules,
minimizing lost packages?

Doesn’t sound much like online
advertising, social networks, or  
any episode of Silicon Valley

What were the origins of machine learning?

• Marvin Minsky @MIT, 1950s

• Support Vector Machines @Bell Labs, 1990s

• Google @Stanford, 1990s

• Ray Kurzweil, 2000s

Nope…
ML, Unpaused

ML has been an aspect of AI research for a  
long while, through several different vectors

A good early history (up to 1980s) is given in:
ML, Unpaused
Machine Learning:A Historical and Methodological Analysis 
Jaime Carbonell, Ryszard Michalski, Tom Mitchell 
AI Magazine 4:3 (1983) 
http://dx.doi.org/10.1609/aimag.v4i3.406
To wit:

task-oriented studies, knowledge acquisition, cognitive
simulation, theoretical exploration … overall, a much  
broader class of optimization problems

An era of anticipation – AI was making inroads…

• emphasis on capturing/representing knowledge  
and expertise – production use cases in medicine

• Fifth Generation Computing (parallel h/w)  
in Japan MCC, etc.

However:

• few outside academia had enough cluster compute
power – aside from 3-letter agencies and AT&T

• meanwhile ML was not yet considered “academic”
enough within academia
Circa early 1980s:

Stock market “corrected” in 1987:
But…

Some fundamental tech platforms emerge…

• Hubble Space Telescope, Human Genome Project,
WWW, electric cars relaunched

And throughout that decade:

• Linux, Java @Sun, JavaScript @Netscape

• Fireﬂy, an initial commercial ML app  
on teh interwebs @MIT Media Lab

• Rise of e-commerce leveraging horizontal  
scale-out with commodity hardware
Circa early 1990s:

Stock market “tumbled” in 2000:
But…

GOOG AMZN EBAY YHOO LNKD NFLX FB TWTR
emerged out of the dust…

• web apps dominated for search, e-commerce,  
social networks, etc.

• did we mention EJBs and template thinking?

• mobile picked up traction

• recommender systems went mainstream

• AI picked up with semantic web efforts…
Circa early 2000s:

Stock market “went free-fall” in 2008:
But…

Successful e-commerce ﬁrms have IPO’ed and are
now busy building skyscrapers in downtown SF…
Circa mid 2010s:
LinkedIn, 350 Bush
Transbay Transit
Salesforce, 415 Mission

An odd truism about the hubris of the uber-wealthy
and the timing of their skyscraper projects…
But…
Sears Tower, Chicago
Lehman Brothers, London
Fontainebleau, Las Vegas

An odd truism about the hubris of the uber-wealthy
and the timing of their skyscraper projects…
But…

Businesses lurv Optimization, lots of it…

• ML circa 1985 focused on those needs, but got
knocked back to something inevitably more
aristotelian and predictable

• Outside of SiliconValley, we’ve made big strides

• One danger: next downturn cycle,VCs might  
reshape tech industry, reverting to “safe bets”
Circa mid 2010s: Back to the Future
However, a few extremely interesting
aspects have emerged…

ETL into
cluster/cloud
data
data
visualize,
reporting
Data
Prep
Features
Learners,
Parameters
Unsupervised
Learning
Explore
train set
test set
models
Evaluate
Optimize
Scoring
production
data
use
cases
data pipelines
actionable results
decisions, feedback
bar developers
foo algorithms
We have approximation, deep learning and
symbolic regression to assist on “Features”
ETL into
cluster/cloud
data
data
visualize,
reporting
Data
Prep
Features
Learners,
Parameters
Unsupervised
Learning
Explore
train set
test set
models
Evaluate
Optimize
Scoring
production
data
use
cases
data pipelines
actionable results
decisions, feedback
bar developers
foo algorithms
Or, maybe, cognitive computing will help on
several of the more difﬁcult aspects of this…
Circa mid 2010s: Extremely Interesting Emerging Aspects

DeepDive @Stanford

http://deepdive.stanford.edu/
Knowledge Graph @Google

http://www.google.com/insidesearch/
features/search/knowledge.html
IBM Watson

http://www.ibm.com/
smarterplanet/us/en/ibmwatson/
Scaled Inference

https://scaledinference.com/

Rhetorical postures: “Is AI a good idea,
or potentially harmful?”  
– per Elon Musk, et al.

Clearly: good idea  
brewbot.io
Rhetorical postures: “Is AI a good idea,
or potentially harmful?”  
– per Elon Musk, et al.

Speaking of which, a highly recommended podcast  
by actual data scientists drinking really good beers:
partiallyderivative.com

2015: Notebooks in Containers in the Cloud
“Keep simple things simple
and complex things possible.”
databricks.com/product
PublishingWorkﬂows for Jupyter

Andrew Odewahn, Kyle Kelley, Rune Madsen

odewahn.github.io/publishing-workflows-for-jupyter
IPython Interactive Demo 
Nature Magazine + Rackspace

nature.com/news/ipython-interactive-demo-7.21492

2015: Notebooks in Containers in the Cloud
“Keep simple things simple
and complex things possible.”
databricks.com/product
PublishingWorkﬂows for Jupyter

Andrew Odewahn
odewahn.github.io/publishing-workflows-for-jupyter
IPython Interactive Demo
Nature Magazine + Rackspace

nature.com/news/ipython-interactive-demo-7.21492
Makes me wonder about the “data engineer”
role … notebooks simplify ops needs, while
ultimately the domain experts wield the real
power with data

Frontstory: The Sun Also Rises
Some wake early in
the morning and go
build buildings
dev-centric templates

Some gaze into the
heavens, sit back,
and explain the
process…
20th c. stats

Sometimes, when
the sky gods become
angry and obscure
the Sun as our due
punishment… VCs during recessions

Others create and
evaluate models to
predict the Earth’s
orbit of the Sun
What’s needed most

Forward Motion:
SV trend: early data scientists displace old-school
product managers

Because there are hard  
problems to be solved…

Because we need  
new eyes on target…

Because use cases…

Because Use Cases: Health Care
“In fact, using ourTopological Data Analysis system, they were
able to discover multiple types of Type 2 diabetes … huge
impact on all the hundreds of millions of people” – Ayasdi
“Nobody knows what to do with those archives …They’re just
sitting there, costing money. This is just seen as a big opportunity.
It’s like,‘Oh, this is what we were saving this up for!’” – Enlitic
“Sloan-Kettering is also trainingWatson on 1,500 real-world lung
cancer cases, helping it to decipher physician notes and learn
from the hospital’s expertise in treating cancer.” – IBM Watson
Employing tech such as deep learning and
cognitive computing for vital use cases in  
health care:

Because Use Cases: Transportation
http://automatic.com/

!
Detects events like hard braking, acceleration – uploaded in
real-time with geolocation to a Spark Streaming pipeline …
data trends indicate road hazards, blind intersections, bad
signal placement, and other input to improve traffic planning.
Also detects inefficient vehicle operation, under-inflated tires,
poor driving behaviors, aggressive acceleration, etc.

Because Use Cases: Education
https://databricks.com/blog/2014/12/08/
pearson…

!
Integrates Kafka + Spark Streaming + Cassandra +
Blur, running within aYARN cluster on AWS to provide
a scalable, reliable, cloud-based platform for services
that analyze student performance across product and
institution boundaries.
Delivers immersive learning experiences
designed for how students read, think,
and learn; as well as efﬁcacy insights to
both learners and institutions which were
not possible before.

!
Reliability features handle Kafka node
failures, receiver failures, leader changes,
committed offset in ZK, plus adjustable
data-rate throughput.

Because Use Cases: Language, everywhere
http://idibon.com/
!
!
!
Our social fabric is encoded as text documents,
and similarly it get tested, deployed, maintained,
and monitored there – it’s the launch point for
cognitive computing.
http://digitalreasoning.com/

http://digitalreasoning.com/
Because Use Cases: Language, everywhere
http://idibon.com/
!
!
!
Our social fabric is encoded as text documents,
and similarly it get tested, deployed, maintained,
and monitored there – it’s the launch point for
cognitive computing.
Robert Munroe, 12:00 “Building Better
Experts: co-optimization of human and
machine intelligence at Idibon”
AndrewTrask, David Gilmore 11:00
“Deep Learning for Natural Language
Processing”

Because Use Cases: Geospatial
Advanced geo uses cases throughout all levels of gov  
and industry for Big Data, machine learning, graph
algorithms, approximations, etc.

If you roll trucks you probably use licenses from ESRI.

Also consider the IoT sensor data, e.g., from National
Instruments' customers – where does it go, what do
organizations use to analyze it?

These are the large-scale optimization problems
you were looking for…
http://esri.github.io/gis-tools-for-hadoop/ (and Spark)
http://thunderheadxpler.blogspot.com/
http://geotrellis.io/
http://www.oculusinfo.com/tiles/
https://databricks.com/blog/2014/12/03/app...

Because Use Cases: Telecom,Travel, Banking, etc.
http://spark-summit.org/2014/talk/
stratio-streaming…

Stratio represents one of the most sophisticated
integrations for Spark Streaming – the union of
a real-time messaging bus with a complex event
processing engine: Kafka, Spark Streaming,
Cassandra, along with the Siddhi CEP engine

Telecom, in particular, is leveraging this new
streaming technology as a big win near-term

http://www.openstratio.org/ 
https://github.com/stratio

https://github.com/Stratio/streaming-
cep-engine
BTW if you’re in Madrid next fall  
check out Big Data Hispano

Because Use Cases…
Common theme: many of those use cases are
powered by Apache Spark –

Especially notice Spark Streaming, which is a big
game-changer for analytics across industry

Because Use Cases…
Common theme: many of those use cases are
powered by
Especially notice
game-changer for analytics across industry
Taylor Goetz 11:00 
“Beyond theTweetingToaster: IoT
Streaming AnalyticsWith Apache
Storm, Kafka, and Arduino”
Hari Shreedharan 12:00 
“RealTime Data Processing Using
Spark Streaming”

Because Use Cases: Agriculture
Ag+Data Issues 
http://radar.oreilly.com/2014/04/agdata.html

Data Guild whitepaper: Ag Systems + Data Outlook 
http://goo.gl/OK8RFf

• livelihood for 40% of world population

• $15T/year annual GDP globally

• data-intensive issues, much legal impasse

Over a half billion small farms worldwide, and most  
are family-run farms that rely on rain-fed agriculture

Nudge, and I just might propose DWave clusters  
into cold craters on the Lunar South Pole with  
routers @L5 and an LLO skyhook… to handle 
the vector quantization demands. Or something.
airships
e.g., JP Aerospace, 40 km
atmostats
e.g.,Titan Aerospace, 20 km
microsats
e.g., Planet Labs, 400 km
robots
e.g., Blue River, 1 m
sensors
e.g., Hortau, -0.3 m
drones
e.g., HoneyComb, 120 m
Layered Sensing Networks

Apache Spark developer certificate program
• http://oreilly.com/go/sparkcert
• defined by Spark experts @Databricks
• assessed by O’Reilly Media
• establishes the bar for Spark expertise
certification:

MOOCs:
Anthony Joseph 
UC Berkeley

begins 2015-02-23

edx.org/course/uc-berkeleyx/uc-
berkeleyx-cs100-1x-
introduction-big-6181
Ameet Talwalkar 
UCLA

begins 2015-04-14

edx.org/course/uc-berkeleyx/
uc-berkeleyx-cs190-1x-
scalable-machine-6066

community:
spark.apache.org/community.html
events worldwide: goo.gl/2YqJZK
!
video+preso archives: spark-summit.org
resources: databricks.com/spark-training-resources
workshops: databricks.com/spark-training

confs:
Strata CA 
San Jose, Feb 18-20 
strataconf.com/strata2015
Spark Summit East 
NYC, Mar 18-19 
spark-summit.org/east
Big Data Tech Con 
Boston, Apr 26-28 
bigdatatechcon.com
Strata EU 
London, May 5-7 
strataconf.com/big-data-conference-uk-2015
Spark Summit 2015 
SF, Jun 15-17 
spark-summit.org

books:
Fast Data Processing  
with Spark 
Holden Karau 
Packt (2013) 
shop.oreilly.com/product/
9781782167068.do
Spark in Action 
Chris Fregly 
Manning (2015*) 
sparkinaction.com/
Learning Spark 
Holden Karau,  
Andy Konwinski,
Matei Zaharia 
O’Reilly (2015*) 
0636920028512.do

presenter:
Just Enough Math
O’Reilly, 2014
justenoughmath.com 
preview: youtu.be/TQ58cWgdCpA
monthly newsletter for updates,  
events, conf summaries, etc.:
liber118.com/pxn/
Enterprise Data Workﬂows
with Cascading
O’Reilly, 2013
0636920028536.do

A New Year in Data Science: ML Unpaused

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