In this deck from the 2019 UK HPC Conference, Glyn Bowden from HPE presents: The Eco-System of AI and How to Use It. "This presentation walks through HPE's current view on AI applications, where it is driving outcomes and innovation, and where the challenges lay. We look at the eco-system that sits around an AI project and look at ways this can impact the success of the endeavor." Watch the video: https://wp.me/p3RLHQ-kVS Learn more: https://www.hpe.com/us/en/solutions/artificial-intelligence.html and http://hpcadvisorycouncil.com/events/2019/uk-conference/agenda.php Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

1. THE ECO-SYSTEM OF AI, AND HOW TO USE IT
Glyn Bowden, HPE Pointnext
CTO of AI, Data and Emerging Technologies
Practice
September 2019

2. Data can transform businesses
Unlocked through AI and advanced
analytics

3. THE WORLD IS REPLACING PROGRAMMING WITH TRAINING
23 million
developers worldwide
Handful of data
scientists
4 in 10 companies mention
lack of analytical skills
as a key challenge
By 2020, 50% of organizations will
lack sufficient AI and
data literacy skills
to achieve business value

4. HPE VISION – AI FROM EDGE TO CLOUD
CONFIDENTIAL 4
Edge Cloud
Where is data
generated?
How long do you
have to take
action?
What governance &
security regulations do
you need to comply
with?
What are your
business goals?
How do you prepare and
integrate data for advanced
analytics?
What does that
data consist of?

5. A simple view
ANALYTICS ACROSS THE DATA PIPELINE
5
Data science tools
Data flow design, data science workbench, model management, application deployment
IIoT / Edge
Analytics at Edge
on data in motion
Data is:
– Acquired, aggregated
– Queued, routed and
orchestrated
– Cached and stored locally
– Applied with rules and
analytic models
Fast data
Core Processing
of data in motion
Data is:
– Ingested
– Restructured, enriched
– Persisted for real time usage
and for offline analytics
– Applied with rules and analytic
models
Big data
Analytics on data at rest
Data is:
– Hosted in a data lake
– Transformed and restructured
– Aggregated
– Molded by rules and models
– Prepped for deep learning
AI
Deep learning /
machine learning
Data:
– Trains/builds analytic models
– Creates test models
Advanced analytics
Traditional business
systems
On premises, at the Edge On premises or cloud

6. THE ANATOMY OF AN AI SOLUTION
6
Training Model
Training Data
Test Data
Live Data
Raw Archive
Data
Transform /
Clean Data
Processing
Data Transport
Transform /
Clean Data
Processing
Inference Model
Activity Function
Visualization
Function
Data Science Workbench / Tooling
Model Packaging
/ Optimisation
Model
Distribution
Data Pipeline
Config
Management
Typically Datacentre / Cloud
Edge or Datacentre / Cloud

7. Build
Train
Deploy
Monitor
HOW DOES HPE ADDRESS THESE CHALLENGES
7
– Containerized sandbox environments
– Choice of ML/DL tools, interfaces
and frameworks
– Secure access to shared data
– Containerized ML/DL environments
– Elastic clusters
– Python, Spark, TensorFlow, ....
Git integration
Model registry
Project repository
– Containerized deployment with scalable
endpoints
– Out of the box runtime engines
– Autoscaling and load balancing
– End-to-end visibility
– 3rd party integrations
Data
Prep
Enterprise grade security and
control

8. HPE ML OPS ARCHITECTURE
HPE ML Ops
NFS HDFS
Compute
Storage
On-Premises Public Cloud
CPUs GPUs
BlueData EPIC Platform
Collaborate
Monitor
Sandbox
Build Deploy
Serving
Train
Training
Data Prep
Data Processing
Data Engineers Data Scientists ML Architects
* 3rd party commercial software requires license independent of HPE ML Ops

9. ML Ops
WHY HPE ML OPS?
Accelerated time to insights with reliable ML pipelines
LA1907258
9
Only enterprise grade solution for entire ML lifecycle
Implement CI/CD, A/B testing, canary testing, endpoints etc.
One container platform for any AI/ML application.
Enterprise security with authentication
Model registry, lineage tracking, auditability, interpretability
Flexibility to deploy on-premises, public cloud, or hybrid
Lifecycle Management
Standardized Processes
Security
Unified Platform
Governance
Hybrid

10. Explore Experiment Evolve
Integrate AI to the
existing applications
Manage
Change, train
your teams and
AI systemsUnderstand
outcomes and
challenges
Conduct a Proof-
of-Value for select
Use Cases
Explore the best use
cases and
technologies
Align teams round
common vision and
plan
Gather, and validate
your use cases and
data
Optimize your
AI technology
Implement
more use cases
NEED TO APPROACH AI IN SIMPLE STEPS

11. EXAMPLE CUSTOMERS
Using natural language
processing to analyse emergency
and non-emergency call
classification to optimise
resource allocation
Protect operator well-being by
monitoring destressing call load
Multiple projects to optimise the
car build as well as provide
trackside insight into race
telemetry
Sensor data analyzed during
testing and racing, informs
changes that should be made to
the car or driving strategy
Leveraging AI driven image
analytics to drive quality
assurance across multiple
factory sites
Converging AI, IT and OT to
boost output and quality
11

12. HPE GreenLake solutions for AI and advanced analytics
AI AS A SERVICE WITH HPE GREENLAKE
FOR HPE AND CHANNEL PARTNER INTERNAL USE ONLY
On your terms
Tailored to your needs
Pay as you go
Economic models aligned
to business models
When you need it
Elastic IT
Consumption
Helping to solve the demands
of business speed
Horizontal AI Infrastructure
and Services for AI/ML

13. THANK YOU
13
glyn.bowden@hpe.com