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IBM Machine Learning Session Agenda
MACHINE LEARNING OVERVIEW
IBM MACHINE LEARNING
IBM MACHINE LEARNING TECHNICAL ARCHITECTURE
IBM MACHINE LEARNING ON Z SYSTEMS
#IBMML
IBM Machine Learning
Rob ThomasGeneral Manager,
IBM Analytics
@robdthomas
SOURCE: http://www.tennessean.com/story/money/tech/2014/05/02/jj-rosen-popular-search-engines-skim-surface/8636081/
Over
of the worlds data cannot be googled
Facebook 56%
Welltower 50%
Alexion Pharmaceuticals 37%
Salesforce.com 32%
Under Armour 30%
TripAdvisor 25%
Priceline 25%
Cognizant 22%
Alphabet 21%
What fuels the growth of outperformers?
A sampling of
the top 10 have
one thing in
common.
AVERAGE REVENUE GROWTH OVER 5 YEARS Machine Learning
SOURCE: Scott Galloway, NYU Stern
PHOTO CREDIT: Kyle Harris
Productivity: Make both
experienced and novice
data scientists more
productive.
Trust: Confidently deploy
insights knowing they are
generated from the most
current data and trends.
Freedom: Choose the
right language and ML
framework and platform
for your business.
Infuse continuous intelligence throughout the enterprise
Continuous Intelligence: Machine Learning made simple
IBM Machine Learning: Extracted from Watson, delivered to your private cloud
IBM PRIVATE CLOUD
Cognitive computing
Augmented intelligence
Machine learning IBM Machine Learning
Unleash Machine Learning on the Worlds
Most Valuable Data
Data and machine learning at work
Behavioral models to identify
clients unique banking needs
Rapidly optimize
algorithms to best fit data
Utilize banks rich client
interaction data
Using behavioral models to offer products that serve customers unique needs
Large Bank Continuous Intelligence in financial services
THE PROBLEM
Banks are losing customers to fintechstartups who use purchased data to develop semi-customized products.
SOLUTION
Customized products offered to customers based on behavioral models using banks rich private data not available to others.
IBMS PRIVATE CLOUD STRATEGY
PRIVATE CLOUD PUBLIC CLOUD
ADVANCED ANALYTICS
Predictive analytics
Machine learning
Decision optimization
Content Analytics
SELF-SERVICE
UNIFIED GOVERNANCE
Spark
Data integration
Data-bases
Data warehouse
Content repositories
Apache Hadoop
Master data management
Metadata management
Lifecycle governance
POWER ECONOMICSCONVENIENCE
OPEN SOURCE
REPOSITORIES
IBM Machine Learning
Flexibility in interfaces
Choice of languages
Richness of algorithms
Multiple execution engines
Choice of platform
Data at speed and scale
Unified capabilitiesFREEDOM
IBM Machine Learning
Collaboration
Automation
User experience
End to end processPRODUCTIVITY
IBM Machine Learning
Deployment
Lifecycle
Feedback loop
Model governance
Built-in expertiseTRUST
Introducing IBM Machine Learning Hub
World-class data science skills
From POC to client engagement
ML research and open source contributors
Education and training, expert advice
HUB
User Integrated Development
Environment (IDE)
Machine Learning Engine
Technical data scientist
IBM Data Science Experience on
Private CloudIBM Machine Learning
IBM Data Science Experience on
IBM Cloud
IBM Watson Machine
Learning
Business data scientist IBM SPSS ModelerIBM SPSS Collaboration
and Deployment Services
How Machine Learning fits in with the IBM Analytics Portfolio
Machine Learning Workflow: The Perception
Data ?Machine
Learning
Algorithm? $
Machine Learning Workflow: The Reality
DataData
Prep
Machine
Learning
Algorithm
Model Deploy $Predict
Creating
Examples
Choosing the
Best Model
Automating Data
Science Work
Scalable
Deployment
Models Lose
Accuracy
MANUAL INTERVENTION
THROUGHOUT
Where much of the worlds most
valuable industry data runs
SOURCE: TBD
First Available for z/OS
Bring machine learning to your
operational data
The worlds leading businesses run on modern mainframes
of the top 50
global banks
of the top 25
biggest retailers
of the top 10
largest insurers
of the worlds
airlines
44 10
18 90%
Data and machine learning at work
Real-time response at
point of sale
Ongoing tracking of
risk profile changes
Co-pay based on
risk profile
Using machine learning to personalize prescription plans & lower payments
Argus Healthcare Continuous Intelligence in healthcare
THE PROBLEM
Develop personalized prescription plan for diabetes patients based on their individual risk profile.
SOLUTION
Classify patients as low, moderate, high risk of developing diabetes based on blood sugar, blood pressure and cholesterol.
CustomerTransaction MerchantExternal: Call
Center
Business
ApplicationsOptimized data Layer
IBM z/OS Platform for Apache Spark
z/OS
Jupyter Notebook
Distributed Apache Spark
Spark Analytic Result Set
Jupyter Notebook
Data DistillationData Distillation
Advantages
Federated analytics across multiple data environments
Increased currency of data & insights reduce latency
Reduce cost and complexity of moving all data
Integration with enterprise business applications
Modern and consistent analytic skill across heterogeneous environment
Provided Under NDA
Analytic Agility with Apache Spark z/OS
For the Linux environment,
we recommend 4 CPs with 8
cores each (32 cores total),
32 GB memory and 250 GB
disk
For z/OS environment, we
recommend allocation of
1GP and 4 zIIPs with 100
GB memory or more to the
LPAR where Machine
Learning for z/OS will run
z/OS Liberty
Application Cluster Ingestion service
Transformation
service
Pipeline
service
z/OS Spark Cluster
Ingestion libTransformation
libPipeline lib
Service
Metadata
ML modelsDB2z
MDSS driver
ML Service UI
Model Management / Model
Deployment / MonitoringBundled softwares
WMLz components
Pre-requisite softwares
zLDAP
RACF
(optional)
Auth
ServiceKubernetes / Docker
Linux
Scoring serviceIMSVSAM
Apache
ToreeJupyter Kernel
Gateway
Metadata
Service
Deployment
Service
(Evaluation/
Monitoring)
Feedback
service
HDFS
LDBM
Jupyter
server
DB2
DB2 JDBC driverCADS/HPO lib
DSX UI (For Data Scientist)
Model creation with
Pipeline UI / Notebook
SMF
CouchDB
(DSX
Metadata)
z/OS
Provided Under NDA
Current Architecture
IBM Machine Learning Session Key Contact Information
MITCHELL [email protected]
KELLY [email protected]
IBM Corporation 2017. All Rights Reserved.
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