data science, realizing the hype cycle. · § models are built using “training” computation...

Luigi Di Rito, Director Data Science Team, SAP Center of Excellence

Data Science, realizing the Hype Cycle.

2

Data Science, Machine Learning and Artificial Intelligence

AREAS OF AI

ArtificialIntelligence

Rule-based Reasoning

Machine Learning

Deep Learning

Natural Language Processing

Translation Machine Vision

Speech to Text

Speech

Text to Speech

Robotics

Autonomics Vehicles

Artificial Intelligence§ Intelligence exhibited by machines§ Broadly defined to include any simulation of human

intelligence§ Expanding and branching areas of research,

development and investment§ Includes robotics, rule-based reasoning, natural

language processing (NLP), knowledge representationtechniques (knowledge graphs), …

`̀

Data Science – akaPredictive/ AdvancedAnalytics§ Algorithmic and computational

techniques and tools forhandling large data sets

§ Increasingly focused onpreparing and modeling data forML and DL tasks

Machine Learning§ A subfield of AI which aims to teach computers the ability to do tasks with data,

without explicit programming§ Uses numerical and statistical approaches, including artificial neural network

techniques to encode learning§ Models are built using “training” computation runs, can also train through usage

Deep Learning§ A subfield of ML that uses specialized computational techniques,

typically multi-layer (2+) artificial neural networks§ Layering allows cascaded learning and abstraction levels (e.g. line

recognition -> shape -> object -> scene)§ Computationally intensive enabled by clouds, GPUs, and increasingly

more specialized HW such as FPGA and new custom hardware

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Is Machine Learning something new?

§ Big Data (for example, cloud applications, the Internet of Things, Data Lakes)

§ Massive improvements in hardware (graphics processing unit [GPU] and multicore)

§ Deep learning and other new algorithms performing complex operations

§ Computers learn from data without being explicitly programmed.

§ Machine learning is not a new concept but is now becomingmainstream

What is machine learning?

Why now?

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Typical Use Cases for Machine Learning

PredictiveMaintenance

PredictiveQuality

ChurnPrediction Demand /

Revenueforecasting

Price / VolumePrediction

PriceOptimization –

Elasticityunderstanding

FraudDetection

CustomerSegmentation

Root-causeanalysis

RiskManagement

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Data Science is an iterative process

CRISP-DM | Cross Industry Standard Process for Data Mining

ModelingData

BusinessUnderstanding

DataUnderstanding

DataPreparation

Evaluation

Deployment

Monitoring

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Is it all about Creating Models?

“Only 20% of the time is spent on modeling / algorithms” Rexer Analytics

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Data PreparationAutomation of data acquisition andtransformation to create input datasets for data science use cases

Advanced AnalyticsUtilize SAP PA automated modelling, HANAPredictive Analytics Library, R, 3rd party and opensource ML, text analytics or geo spatial engines togenerate new business insight

Business Process IntegrationIntegrate results with existingbusiness processes or develop newbusiness applications using SCP, BI,HANA SQL and HTML5 (SAP UI5)

1

2

3

4

Access Storage Preparation VisualizationAnalysis Process Operate

Value

Data AccessIntegrate and transform data from flatfiles, relational data bases or Hadoop forconsumption in statistical models

Real scenarios require a Data Science Platform

8

The Data Scientists Profiles

The DataScientist

The DataArchitect

The DataEngineer/

Administrator

The Data /Analytics Manager

The Data / BusinessAnalyst

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Business ExpertBusiness Analyst

Data Scientist

IT

Data Driven Application Development – Process Steps and Owners

BusinessUnderstanding

DataUnderstanding

DataPreparation

Modelling

Evaluation

Analysis(Production)Data Access Data

Preparation Visualization ProcessIntegration

Operate

SAP DataNon-SAPData

Deploy

Analysis(Exploration)

Data

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INTELLIGENT APPS

Machine Learning @SAP. How do we explain to customers:

DEVELOPER,DATA SCIENTIST

BUSINESS ANALYST,CITIZEN DATA SCIENTIST M

LSK

ILLS#

OF

USER

SMO

DE

1M

OD

E2

PAL, APL,Graph, Text,

Streaming, PAI

Open Source & 3rd PartyMachine Learning

(R, Python, TensorFlow,Spark ML, Hadoop)

Machine LearningCloud Services

SAP PredictiveAnalytics

3rd Party AnalyticalTools

EmbeddedIntelligence(S/4, SAC,

Hybris)

New IntelligentApplications

INTERNAL

December, 2017

Automating predictive quality pipelines based on image featuresSAP Data Hub use case

12PUBLIC© 2017 SAP SE or an SAP affiliate company. All rights reserved. ǀPublic

Efficiently Identifying Faults Earlier

Business Outcome› Reduce waste and rework due to higher accuracy of quality checks

› Early issue detection due to solution performance

Pressure

Temperature

IR image features

ManufacturingMolding press

Sensors

IR cameras

Raw material

ERP Data


SAP Data Hub

SAP HANA Platform

Image ProcessingPipeline

Sensor Pipeline

Predictive

HTML5 User Interface

ERP Data

Feature Extraction

Streaming Data

XSAApplication Server

Business & logistics data

Data Modeling

Orchestration & Pipelines - High Level Architecture


Backend : Data Hub task workflow

2. Extract Features

3. Classify

1. Stream Data

• The workflow simulates the arrival of an image and new sensor data stream

• Process continues with two operators streaming the data to HANA and performing the Python image processingto extract the features

• Finally, a stored procedure in HANA using PAL is used to apply a classification model to evaluate the quality


Backend : Data Hub Pipelines

1. Stream Data


Backend : Data Hub Pipelines2. Extract Features


Backend : Data Hub Pipelines

3. Classify


Frontend : Monitoring UI

Track the products onthe production linewith the quality checkresults

IR Image of theproduction line foroptical validation

Main contributingvariables with theirvalues can be seenhere. If they are overthe limit, it is indicatedby red font

Operator can notifyengineers if someirregularities occur

Thank you.Luigi Di RitoDirector Data Science TeamSAP EMEA Center of Excellence

Thank you!

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data science, realizing the hype cycle. · § models are built using “training” computation...

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