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Realise AI at Scale:Why you need a collaborative ecosystem

© 2018 NetApp, Inc. All rights reserved. — NETAPP CONFIDENTIAL —

John MartinDirector of Strategy and Technology | NetApp4 September, 2018

90% of Data Science projects fail and will never proceed, as Enterprises are unable to provide Data Scientists access to digital systems that can be mined for value.

3 © 2018 NetApp, Inc. All rights reserved. NetApp Confidential – Limited Use Only

§Will we have labour forces to support aging populations?§Will major cities survive rising waters from global warming?§Can we mine resources while minimising human impact on our

planet?§How do we deliver services in congested cities?§How can we maintain quality of life by robotic augmentation?§How do we predict the next major weather event to avoid loss of

life?§How can we more accurately prevent hospitalization?

Mining and Machine LearningA small sample of outcomes and benefits


Plan• Increase

greenfield success rate from 0.5% to 80%+

• Digital twins based on ML for planning and modelling

Drill• Autonomous Drills

have 10% Greater productivity

• Optimal Replacement Time modelling

• Rock fingerprinting

Blast• Improved safety

through flyrockprediction

• Reduced explosive costs through fragmentation prediction

Crush• AI Based sorting

reduces hauling mass by 12%

• Early identification of broken teeth save $100,000’s per incident

Haul• Improved driver

behavior for human operators

• Autonomous Haulers are safer and 15% cheaper to run

Process• Water Usage• 2% Better yields

through temperature and chemical anlysis

Ship• 13% lower

maintenance via prediction of ballast fouling

Improvements in Safety, Speed, Yield and Efficiency Across The Entire Enterprise

The importance of a collaborative Eco-system

§ Connecting the right people with the right problems!

§ Building confidence with quick wins!

§ Solving problems that make a difference – then applying the findings to the same problems in different industries.

§ IR 4.0 is about a better life for all humanity!

§ People, Process Technology!


Line of Business

IaaS/ PaaS

Supplier

Hardware vendors

Business Application Providers -

- No

Vertical Industry

Data Specialists

Data Science

Firm

Deep Learning Specialist

A Real World Example of Ecosystem Collaboration

▪ IaaS / PaaS Supplier - Digital Sense▪ Data Science Firm - Petra▪ Business Application Providers –

Schlumberger etc▪ Deep Learning Specialists - Silverpond▪ Vertical industry data specialist - Interlate▪ Hardware vendors – NetApp and NVIDIA

Australian Mining


Line of Business

IaaS/ PaaS

Supplier

Hardware vendors


- No

Vertical Industry

Data Specialists

Data Science

Firm


Rockface, Subsurface and Lithography Classification

Mineral Prospectivity

Core Drilling Targets

Froth Imagery Classification

Operational Efficiency,

Mineral Output

Downtime,

Assets

Ore reserves

Water UsagePlant Performance

Autonomous Vehicles

Real World Example of Ecosystem Collaboration

Problem - $200m P/A high risk HR cost to protect customer base from competition for no real additional revenue. Value to customer = greater convenience.

▪ Data management company - <TBA>▪ Data Scientists/ML Company - <TBA>▪ Retail Analytics Company - TBA▪ Integration Company - TBA▪ Cloud Company - TBA▪ Warehouse Logistics Start-up - TBA▪ Robot manufacturer Start-up - TBA

Potential outcome - HR cost of only $50m P/A high skilled jobs for Australians, greater value in convenience shopping for customers – less waste.

Australian Retail


Line of Business

IaaS/ PaaS

Supplier

Hardware vendors


- No

Vertical Industry

Data Specialists

Data Science

Firm


NetApp’s Role as a Partner


NetApp Integrated AI Data Pipeline

DATA FABRIC

Cloud AI GPU-aaS / Tiering

Unified

Data

Lake

DATA PREP

1 2 3

Training

Sets

Test

TRAINING CLUSTER

IM3

IM2

IM1

Repo

DEPLOYMENT

CORE

INGEST

EDGE

ANALYZE

CLOUD

Maximize Data Value Independent of Location


Challenge with Scaling AI on PremisesAddressing design, deployment and operations bottlenecks

DESIGN GUESSWORK

DEPLOYMENT COMPLEXITY

MULTIPLE POINTS OF SUPPORT


NetApp ONTAP AIAccelerate your AI data pipeline for deep learning

SIMPLEEliminates design complexity and guessworkPartners deliver complete solution

INTEGRATEDIntelligently manage your data across Edge, Core & CloudDeploy AI Frameworks with confidence

POWERFULScale without limitsStart small and grow non-disruptively


© 2018 NetApp, Inc. All rights reserved. --- NETAPP CONFIDENTIAL -- Limited Use Only 12

Line of Business

IaaS/ PaaS

Supplier

Hardware vendors

Business Application Providers

Vertical Industry

Data Specialists

Data Science

Firm



Architectural models come and go, but data is eternal

Cloud Mirroring

Replicate log objects with prefix sgws://bucket_name/logs/2017-08* to s3://us-east-1.bucket_name to be analyzed in Elastic MapReduce

Bucket Event Notifications

When new objects with prefix sgws://bucket_name/images/* are created, notify my Lambda function to copy the image into AWS S3 and detect features in the scene

Search Integration

Update my Elasticsearch index when new object metadata is written or updated for objects matching prefix sgws://bucket_name/images/*

User Driven Platform Services Empower Users to Implement Hybrid Cloud Data Pipelines with Amazon Web Services

© 2017 NetApp, Inc. All rights reserved. --- NETAPP CONFIDENTIAL ---15

SG

Amazon EMR

Amazon S3

SG

Amazon SNS

Amazon Lambda

1

2

3

Amazon S3

2 3

4

56

1

SG

1 2

Amazon Rekognition

§ Exploratory Analytics is probably the

highest value thing you can do with your

data lake

§ You do feature extraction / assignment

when the data is new for real time

analytics

§ Features are Metadata

§ You cant easily do feature extraction

across Petabytes of cold data

§ Its pretty much impossible to do it on tape

Why metadata becomes more important over time

© 2018 NetApp, Inc. All rights reserved. NetApp Confidential – Limited Use Only16

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