is your it infrastructure ready for machine learning · ready for machine learning ... advanced ml...

Is your IT Infrastructure Ready for Machine Learning & Artificial Intelligence?

Hoseb Dermanilian, EMEA BDM, NetApp

Arnaud BASSALER, CSE, Cisco Systems

BRKPAR-2955

• Introduction

• AI, Machine Learning and Big Data

• What Makes your IT Infrastructure AI Ready?

• The Data Management Challenge

• The Compute Power Challenge

• Why NetApp & Cisco

• Conclusion

Agenda

AI, Machine Learning and Big Data

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

A Very Quick Overview: AI, ML and Deep Learning

• The explosion of AI due to:

• Infinite storage, flood of data and flexible data management features

• Availability of GPUs for parallel processing

• Artificial Intelligence: Machine exhibited human intelligence

• Machine Learning: State of the art AI

• Deep Learning: Enabling advanced ML

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Big Data and the Rise of Deep Learning

• The convergence of big data with AI

• The availability of large datasets provide meaningful learning and results

• Three critical ways in which big data is now empowering AI:

• Big Data Technology: processing and storing large quantities of data efficiently

• Availability of large data sets

• Scale up algorithms (deep learning)

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Is your IT Infrastructure AI Ready?


What Makes your IT Infrastructure AI Ready?

• Different stages with different infrastructure requirements:

• Flexible and Agile

• Able to Manage Data Efficiently

• Accommodate large and varying volumes and types of Data

• Processing Power / Time

• Accept data from different data sources

• Manage data lifecycle (Cloud and On-Premise)

• Scale non-disruptively

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The Data Management Challenge


The Flow of Data in a Deep Learning Pipeline

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Step1: Eliminate Challenges at the Edge

• Large number of ingestion points

• Amount of data generated

• Apply edge level analytics and pass on selective data

• To do this:

• We require high-performance, ultra-low-latency storage at the edge

• Deploy NetApp ONTAP Select near the edge to:

• Edge analytics and reduce the amount of data needs to be sent to core

• Replicate data efficiently reducing bandwidth

• Utilize Storage Efficiency features to further reduce amount of data sent over

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Step2: Eliminate Challenges at the Core

• Data Lakes

• Performance, number of copies, small-file workloads

• Training Cluster:

• Feeding data from the lake to the training cluster

• Deployment:

• Ultra-low latency

• Overall:

• Separate clusters for each stage

• Copy data from one stage to another

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Step3: Eliminate Challenges in the Cloud

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One More Look into The Data Lake

• On-demand analytics with hybrid cloud and multi-cloud deployments

• Rapid provisioning of clusters for test/dev environments

• Efficient backup and replication of Hadoop data

• Automated data tiering to the cloud

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The Compute Power Challenge


AI Sweeping Across Industries

Internet Services Healthcare Media & Entertainment Security & Defense Retail

➢ Cancer cell detection

➢ Diabetic grading

➢ Drug discovery

➢ Medical research

➢ Theft detection

➢ Auto checkout

➢ Targeted marketing

➢ Face recognition

➢ Video surveillance

➢ Cyber security

➢ Video captioning

➢ Content based search

➢ Natural language processing

➢ Virtual and augmented reality

➢ Image/Video classification

➢ Speech recognition

➢ Virtual and augmented reality

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AI : Consumption vs Enablement

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Cisco GPU Accelerated Data Center

Accelerated Analytics & Databases

Artificial Intelligence

GPU-Accelerated DV

Real-Time and Location AnalyticsWindows 10

Virtual Workstations

Deep Learning

Machine Learning

Unified Computing Systems

Virtualization Accelerated Compute

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What is Accelerated Compute?

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate deep learning, analytics, and engineering applications. ... They play a huge role in accelerating applications in platforms ranging from artificial intelligence to cars, drones, and robots.

For Cisco it encompasses two categories:

1. Artificial Intelligence - Deep Learning

2. Accelerated Analytics - Databases

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Cisco GPUs for AI

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Training and Inference

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Cisco UCS AI Offerings

Training

InferencingUCS

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C220 + 2xP4• UCS Inferencing appliance

for Edge

C240 + 4xP4 or 2xP40• More inferencing GPU with

more storage (DC)

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BRKPAR-2955 22

Why NetApp and Cisco


Because of Flexpod!

A pre-validated, flexible platform that features:

• Cisco Unified Computing System

• Cisco Nexus Networking

• NetApp Storage

• Centralized Management with Automation


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Continue Your Education

• Demos in the Cisco campus

• Walk-in Self-Paced Labs

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• Meet the Engineer 1:1 meetings

• Related sessions

BRKPAR-2955 26

Thank you

is your it infrastructure ready for machine learning · ready for machine learning ... advanced ml...

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