The document discusses Dell Technologies' artificial intelligence (AI) and data analytics solutions portfolio. It provides an overview of Dell's solutions for AI/machine learning, IoT/streaming data, augmented analytics/data warehousing, data lakes, and high-performance computing (HPC). The solutions leverage Dell infrastructure along with partner technologies and are designed to address various analytical use cases such as digital manufacturing, life sciences research, and retail loss prevention.

1. 1
Industry Trends in Analytics
Business Intelligence, Data Warehousing, Big Data, Artificial Intelligence
AI and HPC University Roadshow
Bill Wong – Artificial Intelligence and Data Analytics Practice Leader
Bill Kiatipis - High Performance Computing Strategist
Dell Technologies

2. 2
Agenda
 Key Data Trends and Challenges
 Digital Transformation Through Analytics
 HPC Platform Computing
 Summary

3. 3
Higher Education Advanced Analytics Drivers
Enhance Student
Experience to
Attract and
Retain Students,
Improve Post-
Education
Outcomes
Personalized
Learning to Improve
Student Outcome,
Improve Student
Performance and
Graduation Rates
Drive and support
Research,
Partnerships and
Entrepreneurial
Initiatives in Key
Industries
Smarter Campus to
enhance the student
experience and campus
facilities by
transforming the
economic, social, and
technology foundation

4. Q. What are the technology areas where your organization will be spending the largest amount of new or additional funding in 2019? n = 3,086.
Q. What are the technology areas where your organization will be reducing funding by the highest amount in 2019 compared to 2018? n = 2,819. Multiple
responses allowed, excludes “don’t know.”
Source: Gartner “The 2019 CIO Agenda: Securing a New Foundation for Digital Business,” (G00366991)
Data and Analytics Investments Leads New Digital Transformation
Initiatives (Again) for CIOs
Analytics
investments
continue to
increase
Plans to Increase Investment for Digital Transformation

5. 5
The AI Technology Enablers
1950 1960 1970 1980 1990 2000 2010 2020
Cost of Compute
Amount of Data
Artificial Intelligence
Machine Learning
Deep
Learning
- Artificial Intelligence (AI) is human intelligence mimicked by
machine algorithms, examples: Chess, Go, Facial Recognition
- Machine Learning (ML) is a subset of AI algorithms to parse
data, learn from data, and then make a determination or
prediction, example: Spam Detection, Preventative
Maintenance
- Deep Learning (DL) a subset of machine learning algorithms
that leverage artificial neural networks to develop relationships
among the data, examples: Driverless Cars, Cyber-Security
Accelerators
Algorithms
Big Data
Traditional Programming
Machine Learning

6. 6
Expectations
Plateau of
Productivity
Peak of Slope of EnlightenmentInnovationTrigger Trough of Disillusionment
Inflated Expectations
Hype Cycle for Artificial Intelligence
“Narrow" AI is becoming
better than humans at
defined tasks. "General" AI
is still a long way off.”
Time
Plateau will be reached
less than 2 years
2 to 5 years
5 to 10 years
more than 10 years
Deep Learning
Infrastructure Transformation
Autonomous Vehicles
“AI, one of the most
disruptive classes of
technologies, will become
more widely available due to
cloud computing, open
source and the “maker”
(developers, data scientists
and AI architects) community.
While early adopters will
benefit from continued
evolution of the technology,
the notable change will be its
availability to the masses.
As of July 2019
AI PaaS
Artificial General Intelligence
Machine Learning
NLP
FPGA Accelerators
GPU Accelerators
DNN ASICs
Quantum Computing
Neuromorphic Hardware
Computer Vision
Speech Recognition

7. 7
Top 10 Types of Hardware for AI Delivery*
1. Processors (CPU, GPU, FPGA, ASIC)
2. HPC / Supercomputer Infrastructure
3. Communication Network
4. Personal Devices
5. Connected Home Devices
6. AR / VR Head-Mounted Displays (HMD)
7. Drones
8. Robotics
9. Automotive
10.Sensors and Application Components (audio, camera, LiDAR, etc.)
*The Business Impact and Use Cases for Artificial Intelligence, Gartner, 2017
Accelerate
computational
performance
AI-enabled endpoints
AI-enabled autonomous endpoints

8. 8
AI Accelerators
Flexibility Efficiency
“Starting today (November 13, 2019), Microsoft is providing Azure customers
with access to chips made by the British startup Graphcore.”
Microsoft Sends a New Kind of AI Processor Into the Cloud –
https://www.wired.com/story/microsoft-sends-a-new-kind-of-ai-processor-into-
the-cloud/

9. 9
Dell EMC DSS 8440
A Dynamic Machine Learning and HPC Platform
Differentiated Features Benefits
Up to 10 Accelerator cards
including Industry leading
NVIDIA Telsa V100 GPUs
and Graphcore Colossus C2
IPU cards (Post RTS)
Exceptional horsepower in
a 4U chassis
Supports up to 205W CPUs
with accelerators in 35C
environments
Thermally unconstrained
provides flexibility for a
variety of configurations/
environments
Up to 10 drives of local
storage (NVMe and
SAS/SATA)
Accelerated access to
training data
8 x PCIe Gen3 Extensive I/O options for
network/IO traffic
DSS 8440 with
NVIDIA GPUs
DSS 8440 with
Graphcore IPUs

10. 10
Worldwide Artificial Intelligence 2018 Share Snapshot
Dell Technologies' 2018 AI revenue
grew 72.6% to $1.89 billion, from
$1.10 billion in 2017. Dell revenue in
the AI market is primarily from
infrastructure (server and storage).
*IBM's AI revenue grew 19.0% to $2.58 billion, from $2.17 billion in 2017.
Revenue is divided across software, hardware, and services, with services
and hardware significantly larger than software ($349.6M).

11. © Copyright 2019 Dell Inc.11
Computer Vision Analytics Solutions - Examples
• Agent Video Intelligence: automates video analysis to
detect and alert for events of interest, expedite search in
recorded video and extract statistical data from the
footage captured by surveillance cameras.
• BriefCam: provider of Video Synopsis® and Deep
Learning solutions for rapid video review and search,
real-time alerting and quantitative video insights.
Transforms raw video into actionable intelligence.
• Deep Vision AI: highly customizable “deep learning”
platform that provides, facial and object recognition, as
well as behavioral analysis. Used in more specialized
situations where customer has unique models such as
for uniform detection, sanitary processes, foreign objects
in food processing as a few examples.

12. © Copyright 2019 Dell Inc.12
• Eliminate inefficient islands of storage
– Infrastructure consolidation for both clinical and non-clinical workloads
• Scales as data growth and number of instruments,
modalities, and digital clinical applications
increases
• Enable better information sharing
• Accelerate data analytics to gain new insight
• Extends into the cloud
• Prepared for next generation analytics
Dell EMC
Data Lake
Caffe2
Data Lake Storage Platform

13. 13
Dell EMC HPC
Market Leadership
generations of servers and
storage in HPC clusters10
1st HPC cluster
HPC solutions
program officially
launches
Industry’s 1st
HPC Solution
Bundle
#4
Tungsten
Thunderbird
#6
DCS formed
C-series joins
PowerEdge
Stampede
#7
Zenith
System
launched
at Dell
EMC HPC
Innovation
Lab
Dell EMC merger —
Isilon joins HPC
portfolio
1999
2001
2004
2005
2008
2012
2015
2016
Dell EMC AI solutions
announced
2017
2019
Frontera
2018
First systems with DCLC (U.
Michigan) and HDR (OSC)

14. 14
World-class infrastructure in the Innovation Lab
Zenith
• TOP500-class system based on Intel Scalable Systems
Framework (OPA, KNL, Xeon, OpenHPC)
• 424 nodes dual Intel Xeon Gold processors, Omni-Path
• +160 Intel Xeon Phi (KNL) servers.
• Over 1 PF combined performance!
• #396 on Top500, 1.86 PF theoretical peak
• Lustre, Isilon H600, Isilon F800 and NSS storage
• Liquid cooled and air cooled
Rattler
• Research/development system with Mellanox, NVIDIA and
Bright Computing
• 88 nodes with EDR InfiniBand and Intel Xeon Gold
processors
• 32x PowerEdge C4140 nodes with 4x NVIDIA GPUs
Other systems
• 32 node AMD cluster, storage solutions, etc.
13K ft.2 lab, 1,300+ servers, ~10PB storage dedicated to HPC in collaboration with the community

15. 15
Compute/
Storage/
Networking
Verticals/
Use Cases
Processor/
Accelerator
Software&
Frameworks
Management/
Orchestration
Virtualization
Xeon Phi
R840
In-Memory
Analytics
C4140
R740 C6320p
Training
Machine Learning & Deep Learning eco-system – solving real world problems
Xeon FPGA Adapter
Consumption
Models
Math
Libraries
Crest Family
Big Accelerator
system
BigDL
FPGA Adapter
C6420 R740
Inference
Xeon
T640R940XA
Enterprise ISV
ML Software
Open Source Frameworks
hpcWeb
Recommendation Image Smart Disease Predictive Fraud Smart Traffic Threat Inventory
engines classification chatbots Identification marketing Detection Core to Edge IoT Predictions Management
BrightML
BigData & HPC orchestration Containers orchestration
asaka
Bitfusion Accelerator Virtualization & pooling
Service Providers Systems IntegratorsServices Solution BuildBuy
Hyperconverged
appliance
In-Memory
Analytics Training Inference

16. 16
Dell Value-Add for HPC Customers
Dell EMC
Ready Solution
for HPC
Life Sciences
Dell EMC
Ready Solution
for HPC
Research
HPC
Sales
Specialists
Dell EMC
Ready Solution
for
NFS Storage
Dell
Financial
Services
HPC Specific
Executive
Briefings
HPC
Estimation
Tool
HPC/AI
Innovation
Lab
Dell EMC
Annual HPC
Community
Mtg & DellXL
Reference
Architectures
Ready
Solutions
HPC
Resources
&
Tools
HPC
Services
Dedicated
HPC
organization
HPC
Solution
Architects
HPC
Vertical
SME’s
HPC
TSR’s &
OSE’s
Access to
Future
Roadmaps
ProSupport
Add-on for HPC
Remote
Cluster Mgmt
And support
packs
ProDeploy
for HPC
Cloud
Services
Office of CTO
HPC
Visionaries
Consulting
Services for HPC
Dell EMC
Ready
Solution
for HPC
Digital Mfg.
HPC
Power/Cooling
and Cable
Calculators
Partner with
leading suppliers
and channel
partners
Dell EMC
Ready Solution
for HPC
AI
Dell EMC
Ready Solution
for
Lustre®

17. 17
Business needs
Simon Fraser University
needed increased scale and
capacity to compete and excel
globally using big data and big
compute tools.
Solutions at a glance
• Heterogeneous cluster with Dell EMC PowerEdge
C4130/C6320 servers, Intel® Xeon® E5-2650v4/E5-
2683-v4 processors, Intel Omni-Path, NVIDIA®
Tesla® P100 & V100 GPU accelerators
Business results
• Expanded compute, storage and cloud resources for
over 11,000 researchers across Canada
• Ability to run multiple simultaneous jobs of up to 2,600
CPU cores each
• Allows researchers to much more quickly analyze the
DNA of microbes
• Ability to identify infectious disease outbreaks faster
• More rapid tracking and understanding of origins and
spread of infectious disease outbreaks
With greater computational power than all of
Compute Canada’s legacy systems combined, Cedar
is built for big data. The system can support
researchers collecting, analyzing or sharing immense
volumes of data
https://www.youtube.com/watch?v=3RqF8m65r8g
Simon Fraser University
Reduce the impact of infectious disease outbreaks

18. 18
Decision Criteria for AI Infrastructure/Solutions
Data Scientist Perspective
IDC 2018

19. The Digital Future Demands a New Perspective
Cloud First Data First
Infrastructure-centric Business-centric
Takes into consideration:
• Data gravity
• Data velocity
• Data control
• Data privacy and compliance
Driven by:
• Lower infrastructure CapEx
• Offload infrastructure maintenance
• Improve time to market (deployment
time for infrastructure)

20. 20
Analytics Development Challenges and Best Practices
Data Environment Challenge Best Practices
Data Quality Inconsistently formatted across
the organization, often contains
errors and could create biases.
Minimize duplication. Implement Data
Governance strategy and tools to cleanse,
validate and enforce data quality.
Data Silos Inability to leverage data across
the enterprise for analytics
Develop/engineer a common data repository
to access for advanced analytics
New Sources of Data Machine/Deep Learning often
requires large amounts of data,
from various sources, and
formats (unstructured).
Develop a data integration strategy and use
tools to process unstructured data that
requires additional processing, cleansing,
and/or normalization.
Supporting Near Real-
time Requests
Data repositories are challenged
scaling transactional and query-
based workloads together
Consider translytical databases to address
near real-time data response times for
advanced analytical applications.
Privacy, Security, Ethics,
Auditability
Often not considered at design
time and leads to governance
exposures.
Adoption of design principles to provide data
transparency for key stakeholders and
accountability for governance/regulation
organizations.

21. 21
The Value of Dell for AI Infrastructure
- Comprehensive and Scalable AI/Analytics Platform Portfolio
- Workstations, Servers, Clusters, Storage, Networking
- Infrastructure and Data Science and Analytics Expertise
- HPC and AI Innovation Lab
- IoT / Intelligent Video Analytics Lab
- Solution-based Offerings
- Pre-configured AI Ready Offerings
- IoT / Safety and Security and
Thermal Vision Solutions
- GPU Virtualization
- ML Platforms
Infrastructure
Scalability
Reduce
Complexity
Address
Demand
Partner
Ecosystem
Cost
Effective

22. 22
- Appendix -
Dell Technologies
AI and Data Analytics Solutions

23. 23
Dell Technologies AI and Data Analytics Solutions
AI / Machine Learning / Deep Learning
• Domino Data Science Platform Design Document
• HPC for AI and Data Analytics Ready Architecture
• Retail Loss Prevention Ready Solutions
• DataRobot Reference Architecture
• H2O AI Reference Architecture
• Kubeflow Reference Architecture
• OneConvergence Dkube Reference Architecture
• Iguazio Reference Architecture
• Deep Learning with NVIDIA Ready Solutions
• Isilon with NVIDIA DGX-1 Reference Architecture
• Isilon with NVIDIA DGX-2 Reference Architecture
• Isilon with Dell Precision 7920 Data Science Workstation Reference Architecture
• Isilon with Dell EMC DSS8440 Reference Architecture
• Noodle.ai (OEM) Solution Bundle
IoT / Streaming / Machine Data Analytics
• IntelliSite (OEM) Thermal Detection Solution
• Retail Loss Prevention Ready Solutions
• Dell IoT Safety and Security Portfolio
• Real-Time Data Streaming Ready Architecture
• Splunk Enterprise on Dell EMC Infrastructure
• Streaming Data Platform
• ElasticSearch (OEM) Solution Bundle
© Copyright 2020 Dell Inc.
Augmented Analytics and Data Warehouse
• Spark on Kubernetes
• Kinetica (OEM) Solution Bundle
• ThoughtSpot (OEM) Solution Bundle
• Pivotal Greenplum
• Dell Boomi
Data Lake / Unstructured Data Infrastructure
• Microsoft SQL Server 2019: Big Data Cluster Ready Solution
• Cloudera Hadoop Ready Architecture
• Hortonworks Hadoop Ready Architecture
• Kubernetes Containers with Diamanti (OEM) Solution Bundle
• Grid Dynamics Reference Architecture
• Red Hat OpenShift Reference Architecture
HPC Ready Solutions
• HPC Digital Manufacturing
• HPC Life Sciences
• HPC Research
• HPC BeeGFS Storage
• HPC Lustre Storage
• HPC NFS Storage
• HPC PixStor Storage
*Note, some products can deliver capabilities that address multiple use cases
Product Offerings and Technical Collateral for Analytical Use Cases