contemporary analytical ecosystem · • distributed analytics platform hadoop mapreduce,...

C op yr i g h t © 2013 , SAS Ins t i t u te Inc . A l l r i g h ts r eser v ed .

CONTEMPORARY ANALYTICAL ECOSYSTEM

PATRICK HALL, SAS INSTITUTE


• (Optional) History Lesson

• 2015 Buzzwords

• Machine Learning for X

• Citizen Data Scientist

• High Quality, Distributed, Open-Source Analytics

• Closing

Agenda


Data growth

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

1991 1996 2001 2006 2011 2016

Wo

rld

’s D

ata

in

Ze

tta

byte

s


Data growth

(1 zettabyte = 1 billion terabytes)


Typical server hard drive was 500GB with a transfer

rate of 98 MB/sec

In 2008

An entire Disk could be transferred in 85 minutes

Typical Server Hard Drive was 4TB with a transfer rate of 150

MB/sec

In 2013

An entire disk could be transferred in 440

minutes


$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

2000 2005 2010

Average Price 1MB RAM


0

500

1000

1500

2000

2500

3000

3500

4000

1978 1982 1985 1989 1995 1997 1999 2000 2005 2008

CPU Speed in MHz


• Disk capacities are getting bigger, but disks are not spinning faster

• Processors are not running much faster, but they have more cores

• RAM is becoming affordable


So …

• To handle all of this new data we distribute it on clusters of computers

• Most modern analytical architectures take advantage of in-memory, distributed processing


Analyst

Workstation

Data

MPI Based

Software

Client

Data

Software Server

Analyst

• Multicore CPU

• GPU

• Solid state drive (SSD)

Yesterday’s state-of-the-art:


MPI Based

• Distributed storage platform

Hadoop Distributed File System (HDFS)

Massively parallel (MPP) databases

• Distributed analytics platform

Hadoop MapReduce, disk-enabled

SAS® High-Performance Analytics or SAS ® LASR

Analytic Server, in-memory

Spark MLlib, H2O.ai, in-memory

Data ScientistDistributed Data

and Software on

Multiple Servers

Software

Client

Today’s state-of-the-art:


Agenda


• 2015 Buzzwords




• Closing


Buzzwords

• “IoT”

• “Cloud”

Trigger

Peak of Inflated

Expectations

Trough of Disillusionment

Plateau of Productivity

You are here.


Buzzwords: Internet of Things

• Sensor Data?

• Streaming Analytics?

• Privacy?


Machine Learning

Insights

OutData In

Recommendation

Data

Visualization

Image

Recognition

Buzzwords: Cloud Machine Learning Platforms

Security ??


Buzzwords

• “Big Data” You are here.

Trigger

Peak of Inflated

Expectations

Trough of Disillusionment

Plateau of Productivity


• “Hadoop Corporate Adoption Remains Low”

• Death of RDBMS exaggerated

• Big data adoption will require time


Agenda


• 2015 Buzzwords




• Closing


Databases

Statistics

KDD

AI

Computational Neuroscience

Data Mining

Data Science

MachineLearning

PatternRecognition


Data

Min

ing

Machine Learning

TRANSDUCTION

REINFORCEMENT LEARNING

EVOLUTIONARY LEARNING

*In semi-supervised learning, supervised prediction and classification algorithms are often combined with

clustering.

SEMI-SUPERVISED LEARNING

Prediction and classification*Clustering*EM TSVMManifoldregularization Autoencoders

Multilayer perceptronRestricted Boltzmannmachines

SUPERVISED LEARNING

RegressionLASSO regressionLogistic regressionRidge regression

Decision treeGradient boostingRandom forests

Neural networks SVMNaïve BayesNeighborsGaussianprocesses

UNSUPERVISEDLEARNING

A priori rulesClustering

k-means clusteringMean shift clustering Spectral clustering

Kernel densityestimationNonnegative matrixfactorizationPCA

Kernel PCASparse PCA

Singular valuedecompositionSOM

Don’t

know yKnow y

Sometimes

know y


Machine Learning for X

Let X = { Healthcare , Asset Protection , Manufacturing , Energy ,

Government , Security , Text Mining, … }

• There is a desire to apply advances in machine learning more

broadly.


Let X = Healthcare

• Ensemble models for epidemiology

• Predicting hospital readmission

• Looking forward: Electronic Medical Records (EMR)


Let X = Asset Protection

𝑨 = 𝑸𝚲𝑸−1

𝚲𝑖𝑖 = 𝜆𝑖

𝜆2 > 𝑡


Let X = Manufacturing

• Quality Control

• Deep Learning

What is deep learning?

Using a neural network with more than two hidden layers for a supervised or unsupervised learning task

Why is deep important?

Multiple Scales:

Unsupervised training

y x1 x2 x3

1 2.54 1.65 0.02

0 1.14 0.70 0.82

1 0.99 0.51 2.11

⁞ ⁞ ⁞ ⁞

Target vector Input vectors

Unsupervised training

x1 x2 x3

h11 h12

y

x1 x2 x3

h11 h12

x1 x2 x3

Supervised Neural Network Unsupervised Neural Network

(Known as an autoencoder)

Unsupervised training and stacked layers

Many separate, unsupervised, single hidden-layer networks are used to initialize

a larger supervised network in a layerwise fashion

x1 x2 x3 x4 x5

h11 h12 h13 h14

h21 h22 h23

h31 h32

y

x1 x2 x3 x4 x5

h11 h12 h13 h14

x1 x2 x3 x4 x5h21 h22 h23

h11 h12 h13 h14

h11 h12 h13 h14

h21 h22 h23

h31 h32

h21 h22 h23

h31 h32

y

The weights from layerwise

pre-training can be used as

an initialization for

training the entire deep

network!

Let X = Government

Shadow


Let X = Security


Target Layer

Input Layer

Extractable

Features


PROC CLUSTER data=face.sas_gezichten4 method=average

plots=all outtree = face.gezichtentree ccc pseudo;

id name;

RUN;

Let X = Energy Production


h1

h2

h3

h4

h5

Partially Corrupted Input Features

Hidden Neurons

Hidden Neurons

Hidden Neurons

Hidden Neurons

Hidden Neurons

Uncorrupted Output Features Target Layer

Input Layer

Extractable Features


h52

Target 3 Target 4Target 2Target 1

h51

W51 W52 W53 W54


h51 Edge Weights Handwritten Eight


Agenda


• 2015 Buzzwords




• Closing


Citizen Data Scientist

People using data …

• for the good of humanity!

• because their boss said so. (grumble, grumble)


Citizen Data Scientist

• Datakind

• Watson Analytics

• Beyondcore


Agenda


• 2015 Buzzwords




• Closing


High Quality, Distributed, Open-Source Analytics



• Excellent Core,

ETL, and basic

analytics

• RDD Dataframe

• MLlib is immature:

Production

analytics?



• Innovative data

compression

routines

• Full-featured

analytics


Agenda


• 2015 Buzzwords




• Closing


Don’t be afraid

… yet


!!!???!!!

The ‘IT’ folks The ‘Analytics’ folks

I just built 850

new models.

When can you

put them into

production?


DC Data Community Meetup - July 24th

“Playing Nice: Using PMML, Python, R, and SAS for Production Analytics”

SAS Data Mining Communityhttps://communities.sas.com/community/support-communities/sas_data_mining_and_text_mining

Quora Github Twitterwww.quora.com github.com/jphall663 @jpatrickhall

github.com/sassoftware

Where you can find me …

https://communities.sas.com/community/support-communities/sas_data_mining_and_text_mining

http://www.quora.com/

http://www.github.com/jphall663

http://www.github.com/sassoftware

contemporary analytical ecosystem · • distributed analytics platform hadoop mapreduce,...

Documents