sas viya. Примеры проектов на новой платформе · introduce defect...
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SAS Viya. Примеры проектов на новой платформе
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SAS 9.4 high-level architecture
SAS Grid Compute Nodes Middle Tier Client Tier
Server TierData Tier
Variuos Data Sources
SAS Web clients
SASDesktop clients
(Java, .NET)
SASMetadata Cluster
Clustered SAS WebApplication Servers
(Applications and Services)
SAS High-PerformanceAnalytics
Analytical Data Warehouse
SAS LASR Analytics
Data Warehouse
Metadata Tier
SASMobile clients
PostgreSQL SAS Web Infrastructure
Platform Data Server
SAS Embedded Process
SAS Event Stream ProcessingSAS Micro Analytic Server
Streaming data
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Par
alle
l & S
eria
l, P
ub
/ S
ub
, W
eb S
ervi
ces,
MQ
s
Source-basedEngines
Microservices
UAA
QueryGen
Folders
CAS Mgmt
Data Source Mgmt
AnalyticsGUIs
etc…
BIGUIs
EnvMgr
ModelMgmt
Log
Audit
UAAUAA
Data Mgmt GUIs
In-Memory Runtime Engine
In-Database
In-Hadoop
In-StreamSolutions
APIs
Platform
Analytics
Data ManagementFraud and Security Intelligence
Business VisualizationRisk Management
!
Customer Intelligence
Cloud Analytics Services (CAS)
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SAS Viya 3.4
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SAS Viya products and UIs
Customer-written code
Customer-written code
SAS 9.4 M5 products and UIs
SAS 9.4 M5
LASR and / or HPA runtimes
Metadata (WIP)-based mid-tier
MVA runtime
(full functionality)
Other runtimes
(ESP, In-Database)
SAS Viya
CAS runtime
Today’s architecture
SAS/ConnectServer
Microservices-based mid-tier
Viya MVA runtime(minimal
functionality)
SAS 9 “bridge” to SAS Viya
SAS Viya “bridge” to SAS 9
Copyr i g ht © 2014, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
PURE SAS VIYA
USE CASECHINA SEMICONDUCTOR MANUFACTURING COMPANY
Use Case #1 – Quality Control
Process Efficiency
• Efficient in-memory processing
• Reduce time to run weekly QC
checks
• Utilize SAS Visual Analytics to
review results
Use Case #2 – Image Processing
and Deep Learning
• Read in images of wafers
• Apply new SAS Viya CNN
algorithms to identify flaws in wafers
Use Case #3 – Open Source
Interface
Use Python as an interface
Introduce defect image type
Classification which types
➢ Purpose : Detect on the wafer with spatial patterns is usually a cube for the identification of equipment problems or process variations.
Usually, defect image classification capability for different defect types need over 90%accuracy. The challenge is redetection of small size
defect, number of classified defect type. Do defect types classification is key objectives on defect image application. In common, having
around 10-15 different types and how to defect “unknow” using learning process will be other key objectives.
Copyr i g ht © 2016, SAS Ins t i tu t e Inc . A l l r ights reser ve d .
Pa
ralle
l, W
eb S
erv
ices,
MQ
s
Source-basedEngines
Microservices
UAA
Query
Gen
Folders
CAS
Mgmt
Data
Source
Mgmt
Analytics
GUIs
etc.…
BI
GUIs
Env
Mgr
Model
Mgmt
Log
Audit
UAAUAA
Data
Mgmt
GUIs
In-Memory Engine
In-Hadoop
Solutions
APIs
Infrastructure
Platform
PURE SAS VIYA
USE CASESAS
®
Viya™
ARCHITECTURE
Analytics
Data Management
Business Visualization
Cloud Analytics Services (CAS)
In-Database
KEY BENEFITS
1) Access to Hadoop
2) Fast In-Memory data processing
3) Application of Deep Learning (CNN) algorithms
4) Use Python as the primary interface
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SAS Germany‘s first opportunity for Viya
• Germany‘s largest retail company
• About 25% market share in Germany
• 11.500 stores and supermarkets
• ~ 350 000 employees
• ~ 50 billion euro sales volume in 2016
• Long-term SAS customer
• SAS DI Server, Enterprise Miner, Data Loader, STAT/ETS/IML
• Introduced Hadoop to their landscape 2 years ago
- SAS Germany‘s first customer on MapR
Customer Profile
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SAS Viya
• Business unit (Database Marketing) got interested in Viya at the end of 2016
• Data scientists mainly use programming clients (SAS Display Manager, EG)
• Little use of SAS Enterprise Miner GUI
• SAS VDMML seemed to be a good fit in terms of analytical capabilities
• Everything client need seemed to be available
• EDEKA want to use some of the new features (Factorization Machines)
• Some data scientists with Python know-how
• Meet them where they are
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SAS ViyaSolution scenarios
• Viya Single Node
• Recommend to use a separate machine
• SAS 9.4 M5 on theSAS9 side to leverageseamless integrationtools
- Data preparation still done on SAS9 side
20.05.2017, v1.0 (MAK) Option A: SAS 9.4 Plattform & SAS VDMML auf Single -Server (SMP)
Topologie
SAS Clients (9.4 & Viya)Datenquelle(n)
SAP ERP System
Oracle
Teradata
Authentifizierung
Host Authentifizierung für SAS 9.4
AD/LDAP Authentifizierung für
SAS Viya
Hadoop Cluster
MapR Hadoop Platform v5.2
6 Data Nodes
SAS Embedded Process
Shared Storage (optional)
SAS Enterprise Miner Projekte
SAS Data Marts
SAS ABTs
SAS 9.4 M5 Metadaten ServerSAS Enterprise Miner Server - Lizenz läuf im Dezember ausSAS Data Surveyor for SAP - SAS Data Integration ServerSAS/GRAPH; SAS/STATSAS/ETS; SAS/IMLSAS/Access to OracleSAS/Access to TeradataSAS/Access to HadoopSAS Data Loader for HadoopSAS Bridge to Viya
SAS 9.4 M5 SAS Analytics Server4 Cores / 32-64 GB RAMSLES 11 SP3
SAS Cloud Analytics Services (CAS) - In-Memory EngineCAS Controller - CAS Monitor - CAS Client ServicesCAS WorkerCAS Clients - SAS Studio Web Application - SAS Workspace CAS ClientSAS MicroservicesVisual Data Mining and Machine Learning (VDMML)
SAS Viya 3.2/3.3 (VDMML)4 Cores / min. 64-96 GB RAMSLES 11 SP3
SAS 9.4
SAS Management Console
SAS Enterprise Miner
SAS Data Integration Studio
SAS Enterprise Guide
SAS Viya
SAS VDMML (Browser)
SAS VA (Browser)
SAS VS (Browser)
EP EP EP EPEPEP
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SAS ViyaSolution scenarios
• Viya Distributed
• Deploy CAS on MapRdata nodes
• Recommend to increase to 8 cores at least (4x8 cores)
• Deploy Viya support services on SAS9 node
• SAS 9.4 M5 on the SAS9 side to leverage seamless integration tools
For the fiscal year ending in
January 2017, Walmart’s total
Revenue was $485.9 Billion
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• Small Format- Store format optimization- Remodel Optimization- Store Clustering
• Supply Chain- Forecasting Truck demand for 2yr- DC Route Optimization
• Real Estate- Store layout optimization- Site selection for remodels
• Energy- Solar energy production- Energy demand by store by hour
Known High Impact Analytics Projects • Logistics and Transportation
- Ability to leverage existing projects - U.S. Distribution Center Network Coverage
Model- Transportation Optimization
• Forecasting- Forecast Online Sales- Demand Planning and Forecasting for Stores
• Omni Channel Merchandising & Marketing- Customer Analytics- Market Basket Analysis- Trade Area Analytics- Marketing Attribution
Enhanced Analytical Innovation HubLeveraging SAS Open Platform
• Make it easier for users to access environment
• Provide relevant tools to work being completed
• Allow world wide access to environment• Simplicity in managing global platform• Leverage central IT functions
• Unrestricted user access• Additional organizations and users
leverage central environment
Increase utilization of investment in SAS
Improve User Experience
Develop Global Analytics Platform
SAS GridSAS ViyaAnalytics
• Add Small environment of Viya to the below which adds the following benefits;
• Allow for Open Source programming (API’s for R, Python, Java, Lua, REST) that teams already have strong skills in
• Industry leading machine learning, deep learning and natural language processing
• Governance of and standardization on models and analytics
• SAS’s Open Platform Viya is the next iteration of SAS’s industry leading analytics platform that will enable significant future innovation
• Keep same number of cores (64 Cores) – “like for like approach” with fresh architecture and versions of software
• Combine Sunnyvale and Grid environment to provide for large scale forecasting
• License the same software across all grid nodes for ease of deployment and future upgrades
• Provide for high availability & lower cost by virtualizing & clustering SAS Metadata, Midtier and Grid Manager
Open Platform– Including SAS ViyaRecommendations Summary
Platform Goals & Returns
Better Best
WorkersControllers
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&SAS Viya
SAS 9
one SAS platform
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