Upload
others
View
5
Download
0
Embed Size (px)
Citation preview
DigitalEventExperience
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
Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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
Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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
8Think 2020 / May 5, 2020 / © 2020 IBM Corporation
Cloud Pak for Data System
Start small, grow as you need
Cloud-in-a-box Flexible and cost-efficient
Modernize your data center
Think 2020 / May 5, 2020 / © 2020 IBM Corporation
Cloud Pak for Data System
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/
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
Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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
Cloud Pak for Data System
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
Think 2020 / May 5, 2020 / © 2020 IBM Corporation
Native Netezza compatibility
21Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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
Think 2020 / May 5, 2020 / © 2020 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
Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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)
24Think 2020 / May 5, 2020 / © 2020 IBM Corporation
Concerto/kənˈtʃəːtəʊ,kənˈtʃɛːtəʊ/
a high performance, cloud data warehouse running the next generation Netezza database enginedelivered through Cloud Pak for Data
25Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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
27Think 2020 / May 5, 2020 / © 2020 IBM Corporation
Next steps:
Tap into IBM resources and expertise to help drive your business outcomes
28Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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
29Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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.
30Think 2020 / May 5, 2020 / © 2020 IBM Corporation
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.
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.
Thank you
32Think 2020 / May 5, 2020 / © 2020 IBM Corporation
Vikram MuraliDirector of Development, IBM—
®