The path to AI with IBM Cloud Pak for Data System and ...... · Experience Cloud Pak for Data Start your 7-day trial of IBM Cloud Pak for Data. ibm.biz/Bdqzke Take the Data and AI

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The path to AI with IBM Cloud Pak for Data System and Intel Technologies —

Steve LueckVice President, Data Management, Associated Bank

Jeremy Rader Sr. Director of Data Centric Solutions, Intel Corporation

Vikram MuraliDirector of Development, IBM

Think 2020 / May 5, 2020 / © 2020 IBM Corporation

Think 2020 / DOC ID / Month XX, 2020 / © 2020 IBM Corporation

Please note

IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice and at IBM’s sole discretion.

Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision.

The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract.

The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.

Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

Contents

3Think 2020 / May 5, 2020 / © 2020 IBM Corporation

The AI Ladder 05

Cloud Pak for Data 06

Cloud Pak for Data System 07

The Intel Advantage 11

IBM Netezza Performance Server 19Project Concerto 25

Cloud Pak for Data System Roadmap 27

Fireside chat w/ Steve Lueck 28from Associated Bank

81%don’t understand the data required for AI

“No amount of AI algorithmic sophistication will overcome a lack of data [architecture]”

AI pioneers are

8xmore likely to have a robust data architecture

4

There is no AI without IA


Collect - Make data simple and accessible

Organize - Create a business-ready analytics foundation

Analyze - Build and scale AI with trust and transparency

Infuse - Operationalize AI throughout the business

ModernizeMake your data ready for anAI and hybrid cloud world

The AI Ladder

AI

One platform, any cloud.Talent & Skills

Analyze & InfusePlug and play 45+ data, analytics and AI apps.

Manage your favorite open source capabilities along side IBM’s market leading differentiators.

Organize Catalog and govern all enterprise data, models,

rules, and insights through a common experience

OpenShiftLeverage the leading open source hybrid cloud platform to SCALE data & AI workloads.

CollectVirtually connect, manage and query data & AI

assets no matter where they live.

Run on ANY CloudDecoupling enterprise data, analytics and AI will

prevent lock in and accelerate polyglot eco-systems.

IBM Cloud Pak for DataSimplifies, unifies and automates the AI Ladder


Cloud Pak for Data System

7

True plug-and-play enterprise data and AI in under 4 hours right out of the box, securely behind the firewall

– An all-in-one system pre-integrated with all the necessary hardware and software components

– Deploy a complete private cloud data and AI platform in hours, with no assembly required

– Dynamically scale compute, storage and software with on-demand plug and play

– Simplify management and optimization with a unified and intuitive dashboard

Data and AI Forum / © 2019 IBM Corporation Think 2020 / May 5, 2020 / © 2020 IBM Corporation

Time to value mattersJust because you can build a solution from scratch doesn’t mean you should



Start small, grow as you need

Cloud-in-a-box Flexible and cost-efficient

Modernize your data center



9

Cloud Pak for Data SystemA hyper converged system architecture

10

IBM and ISV Add-Ons Custom App Deployments

Netezza Performance ServerCloud Pak For Data Open Shift Catalog*/Customer Applications

VM-Based, containerized, noncontainerized apps

OpenShift (Kubernetes) Container Management

Bare-Metal non-containerized applications

Unified Console,Magneto Private Cloud Management, Call Home

RHEL OS + KVM Virtualization

Highly available Hyperconverged Modular Building BlocksCompute/Storage with Hardware Acceleration, Networking, Management, Power

*Requires customer to bring license of “Container Platform”Think 2020 / May 5, 2020 / © 2020 IBM Corporation

Think 2020 / © 2020 IBM Corporation

12

Challenges Across the Data Lifecycle

Ingest Prepare Analyze ActCore Workflow

challenges

Data growing at extreme pace –61% annually worldwide1

Complexity of edge to core –where to process data and when

to ingest

Data scientists spend ~40% of their time

gathering and cleaning data2

Breaking down silos for unified view of all

corporate data

Documenting and proving out the ROI

or new projects

Complexity of integrating AI with

data pipeline + existing tools

Operationalize new projects and run at

scale

Business adoption of solutions

1. https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html2. https://businessoverbroadway.com/2019/02/19/how-do-data-professionals-spend-their-time-on-data-science-projects/

https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html

https://businessoverbroadway.com/2019/02/19/how-do-data-professionals-spend-their-time-on-data-science-projects/

13

We are working everywhere to

help you overcome these challenges.

Move data faster, store more data, and process everything.

15,000+ software engineers

13,000+ ecosystem partners

14

Delivering an Innovative & Flexible PortfolioAcross the Entire Data Pipeline

Move faster

Ingest ActAnalyze

Process everything

Store more

Prepare

Intel® Ethernet

15

Partners Across the Entire Pipeline

DataCreation

DataTransmission

DataIngestion

DataIntegration

DataStaging Archival Visualize &

DeployTuning &

Distillation

Ingest Prepare Act

Data Clean, Normalize

Models & Experiment

Analyze

Libraries &

toolsVMWAREplatforms IBM RED HAT NUTANIX GOOGLE ANTHOS

software partners

MKL DAAL

AWS AZURE

DNNL

IBM Cloud Pak for Data SystemThe Intel Advantage

16

Intel Add-Ons: Optimized for Performance

up to 11x FASTER AI inferencing1

up to 47x FASTER with software optimizations2

Simplified and performance-optimized

AI and analytics

up to 6x PERFORMANCEusing drop-in replacement

for existing Python3

The Intel Advantage: Accelerate and Secure

IBM + Intel: Driving Your AI Success

Infuse

Analyze

Organize

Collect

IBM’s AI Ladder

Accelerate AI results and time to action with enhanced infrastructure

Speed development and insights with optimized frameworks

Collect and organize data faster with a modernized, high-performance and secure platform that protects data

17

Delivering Complete Cloud Pak Hybrid Cloud Solutions:

Pre-validated optimized reference designs from the hardware ecosystem to simplify procurement and deployment.

Combining Intel, IBM, Red Hat with third party hardware and software for best of breed solutions to common industry problems.

Hardware Certification for Cloud Paks

18

Cloud Pak Systems

Joint Solutions

Pre-integrated hardware and software solutions to reduce time to get up and running.

IBM Cloud Pak System IBM Cloud Pak for Data System

Supply Chain Edge

++

19

High performance analyticsCloud Pak for

Data System now includes Netezza Performance Server


100% compatible with Netezza

– 3x+ faster– Lower operating

costs– Smaller footprint

Fully (100%) compatible with Netezza/Pure Data System for Analytics line of appliance offerings, all nz* utilities compatible, Synergy with data integration & BI tools, “fork-lift” existing Netezza systems, ‘nz_migrate’ and go

Netezza Performance Server vs everyone else: Benefits of upgrading

20

0Migration effort,frictionless upgrade


100%Netezza cross-generational compatibility

IA for AI Blazing fast

0%RiskIn-place

expansion and hardware based multi-tenancy

Managed cloud in roadmap

Built-in security


Native Netezza compatibility


Simple– Load and Go, minimal

administration and tuningCompatible– Based on Netezza

Performance Server, now with 64-bit and NVMe flash drives

– 100% Netezza (Mustang/Skimmer/TwinFin/Striper/Mako) compatibility

– “Fork-lift” existing Netezza/PDA systems

– Simply use familiar ‘nz_migrate’ and go– Existing UDFs / UDXs work

(requires re-compile)– Bi-directional D/R

Replication, BnR with older Netezza/PDA

Smart with built-in FPGA Performance Accelerator and SSDs– Data Stream processing Machine Learning– In-place Advanced

Analytics & Geo Spatial– Familiar interface and

tools with 3rd party tool integration

– Informatica, Tableau, SAS, MicroStrategy, Oracle, Microsoft, SAP, etc.

Compatible

Data virtualization

– Single data fabric for self service

– DV gateway through Fluid Query

Data Virtualization

• High performance / Enterprise Ready• Hybrid data store with extreme functionality

Queryplex service node

ServiceNode

Cluster

Constellation

CachingPolicy

Data SourceNode

AnalyticsApplication

• Single data fabric for self-service• DV gateway through Fluid Query

IBM Performance

Server

23July 2019 © IBM Corporation

Data Virtualization

• High performance / Enterprise Ready• Hybrid data store with extreme functionality

Queryplex service node

ServiceNode

Cluster

Constellation

CachingPolicy

Data SourceNode

AnalyticsApplication

• Single data fabric for self-service• DV gateway through Fluid Query

IBM Performance

Server

23July 2019 © IBM Corporation


– High performance/enterprise ready

– Hybrid data store with extreme functionality

Built-in data science and machine learning

23

Powered by state of the art Data Science Hardware Performance Accelerator

Drive more value from your data with the ability to run in database machine learning models using tools and advanced algorithms already preferred by Data Scientists and analytics professionals


Watson Studio

Integration with model building tools

Integration with BI and visualization tools

In place machine learning andHigh Speed Data Accelerator (FPGA)

INZA (In-database analytics)


Concerto/kənˈtʃəːtəʊ,kənˈtʃɛːtəʊ/

a high performance, cloud data warehouse running the next generation Netezza database enginedelivered through Cloud Pak for Data


IBM Project Concerto

AutomatedMinimal human touch required to provision, scale and maintain the service

Powered by NetezzaThe obvious choice for Netezza upgrades to the Cloud with a simple lift & shift

Scalable & elasticIndependently scale and manage compute & storage with zero disruption

Highly availableCloud-managed compute and highly available cloud storage, atop Red Hat OpenShift

ReliableBackups kept in object storage, and replicated to multiple availability zones

N

25Available on IBM Cloud, Amazon Web Services by mid 2020 with Azure coming in late 2020 through IBM Cloud Pak for Data

Fireside chat –Welcome Associated BankSteve LueckSenior Vice President, Data ManagementAssociated Bank

Experience Cloud Pak for DataStart your 7-day trial of IBM Cloud Pak for Data. ibm.biz/Bdqzke

Take the Data and AI business value assessment

See the additional value your company can expect by accelerating your journey to AI

ibm.biz/Bdqzkb

Engage with our expertsConnect with our Data Science and AI Elite team for a customized 4–6 weeks AI consultation

ibm.biz/Bdqzkn

Learn more about Cloud Pak for Data SystemRead how an optimized hyper-converged data and AI platform can enable faster realization of business insights. ibm.biz/BdqzNL


Next steps:

Tap into IBM resources and expertise to help drive your business outcomes

https://ibm.biz/Bdqzke

https://ibm.biz/Bdqzkb

https://ibm.biz/Bdqzkn

https://ibm.biz/BdqzNL


IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice and at IBM’s sole discretion.

Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision.

The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract.

The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.

Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

Please note


Notices and disclaimers

© 2020 International Business Machines Corporation. No part of this document may be reproduced or transmitted in any form without written permission from IBM.

U.S. Government Users Restricted Rights — use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM.

Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. This document is distributed “as is” without any warranty, either express or implied. In no event, shall IBM be liable for any damage arising from the use of this information, including but not limited to, loss of data, business interruption, loss of profit or loss of opportunity. IBM products and services are warranted per the terms and conditions of the agreements under which they are provided.

IBM products are manufactured from new parts or new and used parts. In some cases, a product may not be new and may have been previously installed. Regardless, our warranty terms apply.”

Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice.

Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary.

References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in which IBM operates or does business.

Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or their specific situation.

It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer follows any law.


Notices and disclaimerscontinuedInformation concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not tested those products about this publication and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products.Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. IBM does not warrant the quality of any third-party products, or the ability of any such third-party products to interoperate with IBM’s products. IBM expressly disclaims all warranties, expressed or implied, including but not limited to, the implied warranties of merchantability and fitness for a purpose.

The provision of the information contained herein is not intended to, and does not, grant any right or license under any IBM patents, copyrights, trademarks or other intellectual property right.

IBM, the IBM logo, ibm.com and [names of other referenced IBM products and services used in the presentation] are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at: www.ibm.com/legal/copytrade.shtml.

http://www.ibm.com/legal/copytrade.shtml

31Think 2020 / DOC ID / Month XX, 2020 / © 2020 IBM Corporation

Intel Corporation – Notices and disclaimers

Performance results are based on testing as of dates shown in configuration and may not reflect all publicly available security updates. See configuration disclosure for details. No product or component can be absolutely secure. Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. For more complete information visit www.intel.com/benchmarks.

1,2. 3.75x improvement with AI Inferencing Intel Select Solution. The solution was tested with KPI Targets: OpenVINO/ ResNet50 on INT8 on 02-26-2019 with the following hardware and software configuration:Base configuration: 1 Node, 2x Intel® Xeon® Gold 6248; 1x Intel® Server Board S2600WFT; Total Memory 192 GB, 12 slots/16 GB/2666 MT/s DDR4 RDIMM; HyperThreading: Enable; Turbo: Enable; Storage(boot): Intel® SSD DC P4101; Storage(capacity): At least 2 TB Intel® SSD DC P4610 PCIe NVMe; OS/Software: CentOS Linux release 7.6.1810 (Core) with Kernel 3.10.0-957.el7.x86_64; Framework version: OpenVINO 2018 R5 445; Dataset:sample image from benchmark tool; Model topology: ResNet 50 v1; Batch Size: 4; nireq: 20. The solution was tested with KPI Targets: TensorFlow/ ResNet50 on INT8 on 03-07-2019 with the following hardware and software configuration:Base configuration: 1 Node, 2x Intel® Xeon® Gold 6248; 1x Intel® Server Board S2600WFT; Total Memory 192 GB, 12 slots/16 GB/2666 MT/s DDR4 RDIMM; HyperThreading: Enable; Turbo: Enable; Storage(boot): Intel® SSD DC P4101; Storage(capacity): At least 2 TB Intel® SSD DC P4610 PCIe NVMe; OS/Software: CentOS Linux release 7.6.1810 (Core) with Kernel 3.10.0-957.el7.x86_64; Framework version: intelaipg/intel-optimizedtensorflow:PR25765-devel-mkl; Dataset: Synthetic from benchmark tool; Model topology: ResNet 50 v1; Batch Size: 80

3. 5.4x faster AI inferencing with BigDL on Apache Spark: https://builders.intel.com/docs/intel-select-solutions-for-bigdl-on-apache-spark.pdf

4 Performance results are based on testing as of July 9, 2018 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure.Testing by Intel as of July 9, 2018. Configuration: Stock Python: Python 3.6.6 hc3d631a_0 installed from conda* NumPy 1.15, Numba* 0.39.0, llvmlite 0.24.0, SciPY 1.1.0, scikit-learn* 0.19.2 installed from PIP*. Intel Distribution for Python 2019 Gold: Python 3.6.5 intel_11, NumPY 1.14.3 intel_py36_5, mkl 2019.0 intel_101, mkl_fft 1.0.2 intel_np114py36_6, mkl_random 1.0.1 intel_np114py36_6, Numba 0.39.0 intel_np114py36_0, llvmlite 0.24.0 intel_py36_0, SciPY 1.1.0 intel_np114py36_6, scikit-learn 0.19.1 intel_np114py36_35. Operating system: CentOS* Linux* 7.4.1708, kernel 3.10.0-693.el7.x86_64. Hardware: Intel® Core™ i7 processor 7567U at 3.50 GHz, 32 GB of DDR4 RAM, 2 DIMMs of 16 GB at 2133 MHz.

https://builders.intel.com/docs/intel-select-solutions-for-bigdl-on-apache-spark.pdf

Thank you


Vikram MuraliDirector of Development, IBM—

®

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The path to AI with IBM Cloud Pak for Data System and ...... · Experience Cloud Pak for Data Start your 7-day trial of IBM Cloud Pak for Data. ibm.biz/Bdqzke Take the Data and AI