AI presentation delivered to various telecommunications firms

1. 1
Digital Transformation Through Analytics
AI and Telecom Transformation
Bill Wong
Artificial Intelligence and Big Data Practice Leader
Dell Technologies

2. 2
Telecom – Key Business and Innovation Drivers
Improve Network
Operations Monitoring
and Management to
deliver efficient, timely
and reliable
management
operations
Grow revenue by
enhancing the
customer experience
and improving fraud
mitigation
Improve cybersecurity
capabilities to reduce
threats to the network
and services
Lower costs of
operations using
predictive maintenance

3. 3
AI Opportunities
Customer Experience
Chatbots can use advanced image
recognition and social data to
personalize sales conversation
Customer Acquisition
Classify customer wallets into micro-
segments to establish finely-tuned
marketing campaigns and provide AI-driven
insights on the next best offers
Network Intrusion / Detection
Analyze data such as IP addresses,
geographic data, email domains, mobile device
types, operating systems, browser agents,
phone prefixes, and more to prevent or
remediate account takeovers
Fraud Mitigation
Real-time analysis to identify and detect
and prevent fraud in all avenues of
commerce including online and in-person
transactions
Industry Application Examples
Analyze Consumers’ Behavior
Campaign And Conversion Analysis
Credit Card Application Approval
Customer service chatbots/routing
Claim Fraud Detection
Evaluate Create Worthiness
Fraud And Credit Risk Analysis
Fraud Detection and more…
Predictive Maintenance
Proactive and predictive maintenance
IOT Analytics
Detect interference in cell
towers and reconfigure to
optimize performance

4. 4
Telecommunications Data Lake
Supporting Digital Transformation through Advanced Analytics
Consumption
Zone /
Data Analytics
Raw /
Landing/
Secure Zone/
Data Ingestion
Documents and
Emails
Web logs,
Click
Streams,
Newsfeeds,
IOT/Sensor
data
Self-Service Dashboards
Advanced Analytics
Sales
Analysts
Consumer Dashboards
Operational Analytics
Data
Scientists
Customers
Marketing
Analysts
Data Governance | Security and Compliance
Enriched /
Discovery Zone /
Data
Transformation
Data Sources
Common Services
Optimized Infrastructure for Advanced Analytics
Chat data
Personas
Tools /
Applications
Data Lake Capabilities
• Provide support for a variety of analytical applications, including self-service, operational, and data science analytics
• Data preparation and integration capabilities to ingest structured and unstructured data, move and transform raw data to
enriched data, and enable data access to for the target user base
• An infrastructure platform optimized for advanced analytics that can perform and scale
OLTP, ERP,
CRP Data
Social Networks
Machine
Generated
Data

5. 5
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

6. 6
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

7. 7
AI Accelerators
Flexibility Efficiency
and many more…

8. 8
Deep Learning Analytics – GPU, Graphcore
Dell Technologies – AI Compute Platforms
Performance
Inference
Data Analytics
Multi-App HPC / ML / DL
C6420pC6420p
R840
DS8440
8+
4
2 - 3
1
Solution price $
C4140C4140
GPU DB Acceleration, AI/ML R940xa
SDS/VDI R740XDR740XD
1:1 CPU/GPU ratio
Highest density of
CPU and memory
with 2 GPUs
GRAPHCORE IPU
XILINUX FPGA INTEL FPGA
NVIDIA GPU
INTEL CPU AMD CPU
GRAPHCORE IPU
XILINUX FPGA INTEL FPGA
NVIDIA GPU
INTEL CPU AMD CPU

9. 9
Bitfusion
Hypervisor
50%
vGPU
30%
vGPU
20%
vGPU
GPU
Vsphere User Defined Virtualization VSphere Network Attached AI
Abstract Pooling, Sharing and Automating
GPUGPU GPU
GPU
GPU
GPU GPUGPU
GPU GPU GPU GPU
GPU GPUGPU GPUGPU GPU
GPUGPU GPU
GPU
GPU
GPU GPUGPU
GPU GPU GPU GPU
GPU GPUGPU GPUGPU GPU
NETWORK
GPUGPU GPU
GPU
GPU
GPU GPUGPU
CLOUD
GPU GPU
GPU GPU
GPU GPU
GPU
GPU GPUGPU GPU
GPU GPUGPU GPU
GPU GPU
Maximize UtilizationMaximize Efficiency
GPU Virtualization Economics

10. 10
Dell EMC Data Science
Platform
Nauta ClaraAI KubeFlow
NVIDIA
EGX
Domino Cassandra HPCaaS Metropolis Spark Jupyter
Bright
Cluster
Manger
Dell-curated
Ansible/
Terraform
playbooks
CNI MetalLB CoreDNS Prometheus NFS provisioner
Helm
Kubernetes
Linux (RHEL/CentOS) + CRI (Docker/containers)
1 https://infohub.delltechnologies.com/section-assets/h18136-tco-analysis-dell-emc-hpc-ra-for-ai-da-sb
On-premises system for HPC, AI and Data Analytics
AI / Machine learning / Deep
learning
PowerSwitch S3148-ON
S5232F-ON cluster switch
PowerEdge R740
management and
compute nodes
PowerEdge C4140
acceleration nodes
DSS 8440 dense
acceleration nodes
Dell EMC Isilon
Dell EMC Ready Solution for HPC BeeGFS Storage
Dell EMC Ready Solution for HPC NFS Storage
© Copyright 2020 Dell Inc.
HPC AI Ready Architecture
One Platform for AI, Data Analytics, and Simulation Workloads
• Simplified operations and lower
cost while enabling new use
cases for users at the lowest
TCO1
• Allow HPC, DA & AI workloads
to execute on the same cluster;
reducing data movements for
faster results
• Run simulation & modeling,
analytics, visualization, and AI
workloads on a common HPC
infrastructure
Software ecosystem

11. 11
AI Magic Quadrants
Data Science and Machine Learning Platforms Cloud AI Developer Services
Data science and machine-learning platforms are defined as:
• A cohesive software application that offers a mixture of basic building blocks
essential both for creating many kinds of data science solution and incorporating
such solutions into business processes, surrounding infrastructure and products.
Cloud AI developer services are defined as:
• Cloud-hosted services/models that allow development teams to
leverage AI models via APIs without requiring deep data
science expertise
The Marketplace
Continues To
Evolve

12. 12
Data Analytics and AI Use Cases – Partner Solutions
IOT / Streaming /
Machine Data Analytics
Deliver Near Real-Time
Analytics
• Analyze IOT / Streaming
data
• Improve IT operations and
security leveraging Machine
Data
• Computer vision
applications
Machine / Deep Learning
Transform the business
with analytical insights
• Data Science / Machine
Learning Platform
• Industry-focused AI
platforms
Data Lake/Unstructured
Data Infrastructure
Improving Data Access
and Agility
• Create an enterprise data
platform for structured and
unstructured data
• ETL offload to lower costs
• On-demand deployment of
container-based
environments
Augmented Analytics
and Data Warehouse
Improve Decision
Making
• Support augmented
business analytics
• Create an enterprise data
platform to support
analytics
• Data integration and
Master Data Management

13. 13
H2O.ai DataRobot
AutoML offerings H2O Driverless AI (commercial) and H2O-3 (open source)
• Good adoption of its open source offering
• Machine Learning Interpretability generates the constructs for the data
scientist to use and explain the results of the models
AutoML offerings enables business users and the Citizen Data Scientist
• Easy to use, you do not need to be a data scientist
• Prediction Explanation: Highlights the features that impact each
model’s decision

14. 14
Accelerate Time From Research To Production With An AI ML Platform
• Micro-services based and full stack data science platform. Decouple
infrastructure from the data pipeline microservices. A code-first
platform ready to integrate any containerized tools and open source
• Accelerate AI development with reusable ML components, and
production-ready infrastructure with native Kubernetes cluster
orchestration and meta-scheduler.
Iguazio
Open and High Performance Data Science PaaS
• Managed & hardened open-source plus 3rd party services and apps
• Secure real-time data sharing enabling collaboration & parallelism
• Minimize CPU, mem, and ops overhead
Cnvrg.io

15. 15
Customer and Employee Health and Safety Solutions
• Detection of persons/objects
• Display showing temperature differences accurate
to 0.1°C
• Alarm in case of exceeding or falling below defined
temperature ranges
• Event Triggers (alarm, network message, activation
of a switching output)
• Temperature range from -40 to +550 °C
•Face Redaction for privacy
Dell Workstation
with NVIDIA
Dell Technologies Surveillance Solutions
- Open Data Lake Platform
- Scalable Infrastructure
- Analytics-ready
Image, Video and Thermal-based AI Applications
Applications
- Fraud Detection
- Loss Prevention
- Workplace Accident Reduction
- Customer Insight
- Public Safety
- Counter Terrorism

16. 16
• 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

17. 17
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)
Evolve to a Data-Driven Business

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

19. 19
• Design and build systems for HPC and
Deep Learning workloads
• Systems include compute, storage,
network, software, services, support
• Integration with factory, software, services
• Power and performance analysis, tuning,
best practices, trade-offs
• Focus on application performance
• Vertical solutions
• Research and proof of concept studies
• Publish white papers, blogs, conference
papers
• Access to the systems in the lab delltechnologies.com/innovationlab
Dell Technologies HPC and AI Innovation Lab

20. 20
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

21. 21
- Appendix -
Dell Technologies
AI and Data Analytics Solutions

22. 22
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