machine learning with sas viya - ottawa sas...

Machine Learning with SAS ViyaPresented By: Sabrina Mancini, Solutions Specialist at SAS

Copyright © SAS Inst itute Inc. A l l r ights reserved.

Presented By: Sabrina Mancini, Solutions Specialist at SAS

Agenda


Overview of Overview of Overview of Overview of

SAS SAS SAS SAS ViyaViyaViyaViyaMachine LearningMachine LearningMachine LearningMachine Learning Machine LearningMachine LearningMachine LearningMachine Learning

in SASin SASin SASin SAS

DemonstrationDemonstrationDemonstrationDemonstration

Overview of SAS Viya


Application Services:

SAS Metadata & Middle Tiers

Products and Solutions:

SAS Visual Analytics

SAS Visual Statistics

SAS Decision Manager

SAS Visual Data Machine & Learning

SAS Data Preparation

Hadoop:Hadoop:Hadoop:Hadoop: Web based Web based Web based Web based

applications:applications:applications:applications:

• SAS Visual Analytics

High Level Conceptual Architecture

APIs/

UIs

Data

Access

ACCESS engines

SAS Viya


SAS Metadata & Middle Tiers

Run-time Engines

Security, Governance, Administration

Cloudera

Horton Works

Relational databases:Relational databases:Relational databases:Relational databases:

Teradata

Oracle

Other sources:Other sources:Other sources:Other sources:

Excel workbooks

HTML files

Text files


• SAS Visual Statistics

• SAS Decision

Manager

• SAS Data

Preparation

• SAS Studio

• SAS Visual Data

Mining and Machine

Learning

Mobile applications:Mobile applications:Mobile applications:Mobile applications:


Mobile

Host EnvironmentsOn-premises, Private, Public,

Hybrid Cloud

Directory Services,

Authentication,

Encryption

MVA CAS MAS

Machine Learning


What is Machine Learning?

Using iterative processes,

machine learning builds models

that automaticallyautomaticallyautomaticallyautomatically adaptadaptadaptadapt with


that automaticallyautomaticallyautomaticallyautomatically adaptadaptadaptadapt with

little or no human intervention.

Machine Learning and Inferential Statistics

There is a lot of overlap!

Machine LearningMachine LearningMachine LearningMachine Learning

Results-driven


• Results-driven

• Black box analytics

Inferential StatisticsInferential StatisticsInferential StatisticsInferential Statistics

• Inferential

• White box analytics

Machine Learning Accuracy and Interpretability

• Machine learning automates the

model building process by:

• learning iteratively from data to

identify patterns and

• predicting future results


• predicting future results

with minimal human intervention

• Machine Learning emphasizes more

on predictive accuracy rather than

interpretability of the models

Sample dataSample dataSample dataSample data

Traditional regressionTraditional regressionTraditional regressionTraditional regression

Neural Network Neural Network Neural Network Neural Network

S U P E R V I S E D L E A R N I N G

MACHINE

LEARNING

CATEGORIES

Copyright © SAS Inst itute Inc. A l l r ights reserved.Copyright © SAS Inst itute Inc. A l l r ights reserved.

S E M I - S U P E R V I S E D L E A R N I N G

U N S U P E R V I S E D L E A R N I N G

R E I N F O R C E M E N T L E A R N I N G

Your chosen technique depends on

your problem and your data

Supervised Learning

Trained on labeled observationsTrained on labeled observationsTrained on labeled observationsTrained on labeled observations

Has a target variableHas a target variableHas a target variableHas a target variable

Classification, PredictionClassification, PredictionClassification, PredictionClassification, Prediction

Algorithms: Logistic Regression, Gradient Boosting etc.Algorithms: Logistic Regression, Gradient Boosting etc.Algorithms: Logistic Regression, Gradient Boosting etc.Algorithms: Logistic Regression, Gradient Boosting etc.


Unsupervised Learning

Trained on unlabeled observationsTrained on unlabeled observationsTrained on unlabeled observationsTrained on unlabeled observations

No target variableNo target variableNo target variableNo target variable

Clustering, Feature ExtractionClustering, Feature ExtractionClustering, Feature ExtractionClustering, Feature Extraction

Algorithms: KAlgorithms: KAlgorithms: KAlgorithms: K----means clustering, PCA, etc.means clustering, PCA, etc.means clustering, PCA, etc.means clustering, PCA, etc.


Semi-Supervised Learning

Uses labeled and unlabeled observationsUses labeled and unlabeled observationsUses labeled and unlabeled observationsUses labeled and unlabeled observations

Classification, Regression, PredictionClassification, Regression, PredictionClassification, Regression, PredictionClassification, Regression, Prediction

Algorithms: Autoencoders, TSVM etc.Algorithms: Autoencoders, TSVM etc.Algorithms: Autoencoders, TSVM etc.Algorithms: Autoencoders, TSVM etc.


Reinforcement Learning

A machine learning technique where the goal is to learn a behaviour strategy that A machine learning technique where the goal is to learn a behaviour strategy that A machine learning technique where the goal is to learn a behaviour strategy that A machine learning technique where the goal is to learn a behaviour strategy that

maximizes the long term sum of rewards in an unknown and stochastic environmentmaximizes the long term sum of rewards in an unknown and stochastic environmentmaximizes the long term sum of rewards in an unknown and stochastic environmentmaximizes the long term sum of rewards in an unknown and stochastic environment....


Optimization + Optimization + Optimization + Optimization +

Unlike standard supervised learning, correct input/output pairs need not be presented, and

sub-optimal actions need not be explicitly corrected. Instead the focus is on performance,

which involves finding a balance between exploration (of uncharted territory) and exploitation

(of current knowledge).

Modern Machine Learning ModelsThe black box


How can we generate models which are

not only accurate, but also:

FairFairFairFair

AccountableAccountableAccountableAccountable

TransparentTransparentTransparentTransparent

TrustworthyTrustworthyTrustworthyTrustworthy

ExplainableExplainableExplainableExplainable

????

Interpretability Enables Trust in AI ModelsFigure out when NOT to trust a model

You are detecting

snow, not wolves!

I can’t trust you

Prediction accuracy

is very high. It is time

to put this system

online.


Neural network

to predict

wolf vs husky

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin.

ACM SIGKDD, 2016

Data scientist

QuestionQuestionQuestionQuestion TechniqueTechniqueTechniqueTechnique

What are the top inputs? Variable Importance(VI)/Relative VIVariable Importance(VI)/Relative VIVariable Importance(VI)/Relative VIVariable Importance(VI)/Relative VI

How do the drivers work? Partial Dependence (PD)Partial Dependence (PD)Partial Dependence (PD)Partial Dependence (PD)

Post-Modeling DiagnosticsInput-Output Relationship

• Model agnostic


How do the drivers work? Partial Dependence (PD)Partial Dependence (PD)Partial Dependence (PD)Partial Dependence (PD)

Individual Conditional Expectation (ICE)Individual Conditional Expectation (ICE)Individual Conditional Expectation (ICE)Individual Conditional Expectation (ICE)

What is the explanation for a

particular prediction?

LocalLocalLocalLocal InterpretableInterpretableInterpretableInterpretable ModelModelModelModel----agnosticagnosticagnosticagnostic

ExplanationsExplanationsExplanationsExplanations ((((LIME)LIME)LIME)LIME)

• Model agnostic

• Visual

• Can be used to

compare models

Machine Learning WorkflowBest Practices

1.1.1.1. DefineDefineDefineDefine thethethethe problemproblemproblemproblem: What data is available, and what are you trying to predict? Will

you need to collect more data or hire people to manually label a dataset or use a

pre-trained model to initialise your weights?

2.2.2.2. IdentifyIdentifyIdentifyIdentify aaaa waywaywayway totototo reliablyreliablyreliablyreliably measuremeasuremeasuremeasure successsuccesssuccesssuccess onononon youryouryouryour goalgoalgoalgoal. For simple tasks, this may

be prediction accuracy, but in many cases it will require sophisticated domain-


be prediction accuracy, but in many cases it will require sophisticated domain-

specific metrics.

3.3.3.3. PreparePreparePreparePrepare thethethethe validationvalidationvalidationvalidation processprocessprocessprocess thatthatthatthat you’llyou’llyou’llyou’ll useuseuseuse totototo evaluateevaluateevaluateevaluate youryouryouryour modelsmodelsmodelsmodels. In particular,

you should define a training set, a validation set, and a test set.

4.4.4.4. EnrichEnrichEnrichEnrich andandandand randomizerandomizerandomizerandomize thethethethe datadatadatadata by shuffling the training data and applying random

augmentations/filters depending on the use case (normalization, convolutions

etc). Start with a random sub-set of the data while designing the architecture.

Machine Learning WorkflowBest Practices (cont.)

5555.... DevelopDevelopDevelopDevelop aaaa firstfirstfirstfirst modelmodelmodelmodel thatthatthatthat beatsbeatsbeatsbeats aaaa trivialtrivialtrivialtrivial commoncommoncommoncommon----sensesensesensesense baselinebaselinebaselinebaseline, thus

demonstrating that machine learning can work on your problem. This may not always

be the case!

6.6.6.6. GraduallyGraduallyGraduallyGradually refinerefinerefinerefine youryouryouryour modelmodelmodelmodel architecturearchitecturearchitecturearchitecture bybybyby tuningtuningtuningtuning hyperhyperhyperhyper----parametersparametersparametersparameters andandandand addingaddingaddingadding

regularizationregularizationregularizationregularization. Make changes based on performance on the validation data only,

not the test data or the training data


not the test data or the training data

7.7.7.7. BeBeBeBe awareawareawareaware ofofofof validationvalidationvalidationvalidation----setsetsetset overfittingoverfittingoverfittingoverfitting whenwhenwhenwhen turningturningturningturning hyperhyperhyperhyper----parametersparametersparametersparameters: the fact that

your hyperparameters may end up being overspecialized to the validation set.

Avoiding this is the purpose of having a separate test set!

8.8.8.8. MostMostMostMost commoncommoncommoncommon wayswayswaysways totototo preventpreventpreventprevent overfittingoverfittingoverfittingoverfitting::::

• Get more training data

• Reduce architecture complexity

Machine Learning in SAS


There are Multiple Ways

How Do I Use Machine Learning in SAS Viya??

Visual StatisticsVisual StatisticsVisual Statistics


SAS Studio SAS Visual Analytics SAS Model Studio

VDMML VDMML VDMML

Visual StatisticsVisual StatisticsVisual Statistics

SAS Visual Data Mining and Machine Learning

• Utilizes ML and Deep Learning ML and Deep Learning ML and Deep Learning ML and Deep Learning Models to

analyze structuredstructuredstructuredstructured and unstructuredunstructuredunstructuredunstructured data


analyze structuredstructuredstructuredstructured and unstructuredunstructuredunstructuredunstructured data

• Combines advanced analytics, data prep, advanced analytics, data prep, advanced analytics, data prep, advanced analytics, data prep,

visualization, model assessment, and visualization, model assessment, and visualization, model assessment, and visualization, model assessment, and

deploymentdeploymentdeploymentdeployment in single environment

• Supports a GUI and Coding InterfaceSupports a GUI and Coding InterfaceSupports a GUI and Coding InterfaceSupports a GUI and Coding Interface

SAS Visual Data Mining and Machine Learning

• Brings together allallallall the members of an


• Brings together allallallall the members of an

analytical team in one openopenopenopen and

collaborativecollaborativecollaborativecollaborative environment

• Supports Open Source programming Open Source programming Open Source programming Open Source programming

languages

In Memory Algorithms

STATISTICSSTATISTICSSTATISTICSSTATISTICS MACHINE LEARNINGMACHINE LEARNINGMACHINE LEARNINGMACHINE LEARNING DEEP LEARNINGDEEP LEARNINGDEEP LEARNINGDEEP LEARNING

Cox Proportional Hazards

Decision Trees

Design Matrix

General Additive Models

Bayesian Networks

Boolean Rules

Factorization Machines

Frequent Item Set Mining

Deep Forward Neural Networks (DNNs)

Convolutional Neural Networks (CNNs)

Support VGG-like, ResNet models


General Additive Models

Generalized Linear Models

K-means and K-modes Clustering

Linear Regression

Logistic Regression

Nonlinear Regression

Ordinary Least Squares Regression

Partial Least Squares Regression

Pearson Correlation

Principal Component Analysis

Quantile Regression

Shewhart Control Chart Analysis

Frequent Item Set Mining

Gradient Boosting

K Nearest Neighbor

Market Basket Analysis

Moving Windows PCA

Network Analytics/Community Detection

Neural Networks

Random Forest

Robust PCA

Support Vector Data Description

Support Vector Machines

Text Mining

Variable Clustering

Support VGG-like, ResNet models

Recurrent Neural Networks(RNNs)

Support LSTM, GRU model

Autoencoders for neural networks

Image processing extensions

Augment image action

Convert image table action

Match images action

2D/3D medical image visualization

S

Machine Learning and Deep Learning

S

CSV file, NFS

Cloud or On-Premise

Sources

APIs / REST

Python, R, LUA,

JAVA, Scala SAS Clients


S

S

S

SAS

Machine Learning,

Deep Learning

VDMMLVDMMLVDMMLVDMML

S

SAS

Visual Analytics

VAVAVAVA

Social Data

Oracle

Hadoop

Excel

S

SAS

DATA CONNECTOR

SAS ACCESSSAS ACCESSSAS ACCESSSAS ACCESS

DemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstrationDemonstration

SAS Government Analytics Leadership Forum: SAS Government Analytics Leadership Forum: SAS Government Analytics Leadership Forum: SAS Government Analytics Leadership Forum:

May 16, 2019May 16, 2019May 16, 2019May 16, 2019

Register at the Front Desk or Online!Register at the Front Desk or Online!Register at the Front Desk or Online!Register at the Front Desk or Online!

VDMML Workshop: June 5VDMML Workshop: June 5VDMML Workshop: June 5VDMML Workshop: June 5thththth at the SAS Officeat the SAS Officeat the SAS Officeat the SAS Office

Sign up online!Sign up online!Sign up online!Sign up online!Sign up online!Sign up online!Sign up online!Sign up online!

machine learning with sas viya - ottawa sas...

Documents