Operationalize Machine Learning for Real Impact · Cisco solutions to operationalize machine...

Zongjie Diao, Director of Product Strategy and Management, Cisco

Oct 2019

Operationalize Machine Learning for Real Impact

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential

CIO’s top agenda

Gartner 2019 CIO Survey vs 2018 CIO Survey


AI Initiatives Seen Across Industries

Video Captioning

Content Based Search

Nl, Vr and Ar

Media and

Entertainment

Cancer Cell Detection

Drug Discovery

Medical Research

Healthcare

Fraud Detection

Cryptocurrencies

Algorithmic Trading

Finance

Face Recognition

Crowd Analytics

Cyber Security

Security and

Defense

Theft Detection

Auto Checkout

Targeted Marketing

Retail

Reduce Product Defects

Increase Production Speed

Shorten Downtime

Manufacturing


useless… and a waste of money

Machine Learning is

So said many companies who have tried…..


35%

50%

15%

How often are trained model deployed?

Rarely Often AlwaysUp to 85% trained models may never

be used

https://www.iianalytics.com/2019-analytics-predictions-and-priorities

Low deployment


Long deployment

Expected AI

deployment

timeline

How long it really takes

Start planning

Still planning

Piloting

Restarting Finally launch

3-5X


Source: forrester.com/predictions (2018)

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-frontier-modeling-the-impact-of-ai-on-the-world-

economy?cid=other-eml-alt-mgi-mck-oth-1809&hlkid=5ebe957bb3594f96bedda5695e4664fd&hctky=10366723&hdpid=677435cb-04b0-

445e-afba-4588aa47d2fe

The success of deployment

matters.

The speed of deployment

matters.

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential

• Major ML deployment challenges

• Cisco solutions to operationalize machine learning

Agenda

© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential #DataSymposium2019© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential

• Major ML deployment challenges

• Cisco solutions to operationalize machine learning

Agenda


1

2

3

"Designed by brgfx / Freepik"


Which process(es) below is (are) the most critical

component(s) of ML deployment?

A. Data collection B. Data preparation

C. Training and evaluation D. Inference and

deployment

1


Collect

Clean

Correlate

Train

Data Pipeline for Multiple Data Sources

Collect Clean Correlate Train Data

Model

Result

Collect Clean Correlate Train Model

Collect Clean Correlate Train Model

Social

Video

Model

More

Data


Who are the key stakeholders to ensure a

successful ML deployment at scale? (select

multiple choices)

A. Data scientists B. Data engineers

C. CXO &Line of

business D. IT

2



Framework and SW instruments need to be supported

ML/DL Framework /

Infrastructure

Data

Infrastructure

Inferencing &

Ingestion End

Point


Data Scientists

Data Engineers

IT Team

• Rapidly evolving open source ML frameworks

• Start with workstation or cloud

• Hard to scale for production

• Want cloud like experience on-prem

• Lack of AI/ML expertise

• Need new infra architecture

• Need to solve silos and manageability

• Need enterprise readiness and security

Major challenge gaps exist between key stakeholders in realizing ML impact

CIOs Business

Leaders


How much percent of Machine Learning models are

developed on Cloud vs. on-premise?

A. 90% vs. 10% B. 70% vs. 30%

C. 40% vs. 60% D. 10% vs. 90%

3


The benefits of both – it’s a hybrid world when rubber meets the road

ML model

development

~60% ~40%

Source: Gartner, Market Guide for Machine Learning Compute Infrastructures, Sep 2108, ID: G00362287; Figure 3

Data gravity and integration

Performance

TCO

Governance

Remodel, retraining

At scale production

Fast deployment

Test-dev

Simplicity

APIs

One time

Cloud On-Prem


Cloud Experience On-prem deployment

Gap 2:Consistent machine learning experience across cloud and on-prem


1

2

3

"Designed by brgfx / Freepik"


What can Cisco do?


Data scientists / engineers IT

Cloud experience On-prem deployment

Dedicated/Silo’d

infrastructure

Shared/Integrated

infrastructure

“Operationalizing Machine Learning” is key to realize the real impact of AI/ML - Cisco is here to bridge the gap


Data

Engineer

s

Data

Scientist

s

IT Team

+

HX Turnkey Starter Kit

for dev and

deployment

UCSD Workflow

˝

UCS C480 ML

UCS/HX C220 Servers

UCS C240 Servers

HyperFlex GPU Nodes

Inference Test/Dev Deep Learning

Cisco Solutions

IT needs to be part of the design team for AI/ML from the beginning

NGC


UCS C480 ML Rack Server A No-Compromise Purpose Built Server for Deep Learning

8 SXM2 Nvidia V100 GPUs

NVlink GPU Interconnect

Latest Intel Processors

UCSM & Intersight managed

Up to 24 drives with 6 NVMe


HyperFlex 4.0 for Inferencing on the Edge

Video Customers Customer

Sentiment

Video Inferencing

HyperFlex 4.0


Cisco Hybrid Cloud

Cisco is #2 contributor to

Kubeflow with 2.5M lines of code

HyperFlex

Data Collect Clean Correlate Train

UCS + Cloud On Prem


Cisco: Unified Architecture

App Data Source Storage ML Process Inference

Common Ethernet Fabric

Collect data

Prepare Data

Train models

Evaluate models

Deploy & Improve

Cisco CCP Nvidia vGPU

+

Dedicated Shared

Data Scientists Experience

• Dedicated workstation vs. Share ML infrastructure

Data Center Architecture

• Silo ML infra vs. Integrated Data Center Architecture

Networking Architecture

• InfiniBand vs. Scalable Ethernet


Cisco UCS Cisco HyperFlex Cisco ACI Cisco Multicloud Portfolio

Cisco AI/ML- A Holistic Approach: Edge to Core

Accelerated Computing

for Inference at the Edge

Accelerated Computing

for AI/ML in the Core

Big Data/Analytics

and ERP

AI/ML Stack

Partnerships


Let’s see how Cisco is helping keep the world safe, clean

and convenient through our ML portfolio and eco-system.


Let’s see how Cisco is helping keep the world safe, clean

and convenient through our ML portfolio and eco-system.



Smart City Solution Customer Use Case Example

Sm

art

Cit

y i

n

a B

ox

Ze

ro t

ou

ch

Pro

vis

ion

AI/

ML

Dee

p L

ea

rnin

g

Citizens

Digitization

Smart Parking

Security & Safety

Cities

Solid Waste Management

Intelligent Traffic, Water Mgmt.

Inte

llig

en

t D

ata

Mg

mt.


Cisco DC Solutions for Smart City

Smart City in a box

Governance

City Automation

Safety & Security

Secure Delivery of Apps

Visual Analytics

Cisco Hyperflex Anywhere,

Cloud to DC to Edge

Cisco VSM Surveillance,

S3260, CVLT, Cohesity

Cisco UCSM,

UCSD, CWOM

C480 ML Deep Learning

C240 M5 Inference

Single Console

Cisco Cloud Center


Cisco Security Architecture for Smart Cities

Awiros

Cross Vertical(parking , Lighting)

Kinetic for City - HX

Edge

Camera

Analytics

Camera

Video Surveillance - 3260

Video Analytics ( AI/ML) – 480 ML

Software

Predictive Analytics

Data Aggregation

and Normalization

Analytics

Sensor Management

Sensor / Camera

Alerts

and Events

Alerts

and Events

Alerts and Events

Alerts and Events

1

2 2 1

2

4

5 Dashboard ( GeoShield)

Camera Camera


https://www.cisco.com/c/en/us/solutions/data-center/artificial-

intelligence-machine-learning/index.html

[email protected]

Cisco DevNet, Cisco DMZ Lab

Operationalizing ML is

the key to success!

Cisco has solutions and is ready to

help!

https://www.cisco.com/c/en/us/solutions/data-center/artificial-intelligence-machine-learning/index.html










Thank you!

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