The document outlines 5 key lessons learned from deploying AI in the real world: 1. AI is a data pipeline requiring ingestion, cleaning, exploration, and training of data. 2. Throwing all data into a data lake without organization makes it difficult to take advantage of opportunities in the data. 3. Whether to use cloud or on-premises solutions for AI depends on where you are in the exploration or production phases of your project. 4. Benchmarks often do not reflect real-world performance of AI systems due to simplifications made in testing. 5. An ideal data platform is a dynamic data hub that can handle a variety of data access patterns and scale elastically for

1. TOP 5 LESSONS
LEARNED
IN DEPLOYING AI IN THE
REAL WORLD

2. © 2018 PURE STORAGE INC.2
QUESTION ON EVERYONE’S MIND:
WHY IS A STORAGE COMPANY TALKING
ABOUT AI?

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NEW ALGORITHMS
Massively Parallel Delivering
Superhuman Accuracy
MODERN COMPUTE
Massively Parallel Architecture
Driving Performance
GPU- THOUSANDS OF CORES
BIG DATA
Data is the New Oil
50 Zettabytes Created in 2020
EXPLOSION IN ARTIFICIAL INTELLIGENCE
FUELED BY PARALLEL COMPUTE, NEW ALGORITHMS, AND BIG DATA

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FRAMEWORKS GPU SERVER STORAGE
TECHNOLOGIES OF THE BIG BANG
WHAT CUSTOMERS DEPLOY

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DATA IS VITAL TO MACHINE LEARNING
OBSERVATION BY PROF. ANDREW NG, AI LUMINARY

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“We don’t have better algorithms,
we just have more data”
PETER NORVIG
Engineering Director, Google

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The AI “hierarchy of needs”
credit: Monica Rogati
ML algorithms: linear & logistic
regression, k-means clustering, decision
trees, etc.
Validation: A/B testing, detecting model
drift over time✓
Data preparation: cleaning, feature
identification, exploration, etc.
Data acquisition: ingest, transformation,
and representation of data for analysis

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TOP 5 LESSONS LEARNED
1.AIisaDataPipeline

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WHAT MOST THINK IS AI
NEW POSSIBILITIES
For Nearly Every Industry
FRAMEWORKS
To Get Started
GPU
The Engine

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AI IS SO MUCH MORE
“Hidden Technical Debt in Machine Learning Systems”, Google NIPS 2015

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COMPLEXITIES OF AI IN PRODUCTION
INGEST
From sensors, machines,
& user generated
CLEAN &
TRANSFORM
Label, anomaly detection, ETL,
prep, stage
EXPLORE
Quickly iterate to
converge on models
TRAIN
Run for hours to days in
production cluster
CPU Servers GPU Server GPU Production Cluster
COPY &
TRANSFORM
COPY &
TRANSFORM
COPY &
TRANSFORM

12. © 2018 PURE STORAGE INC.12
WIDE RANGE OF NEEDS IN AI PIPELINE
SIGNIFICANT CHALLENGE TO LEGACY STORAGE
INGEST
From sensors & machines
CLEAN &
TRANSFORM
CPU Servers
EXPLORE
GPU Server
TRAIN
GPU Production Cluster
Access Pattern sequential sequential or random random random
Access Type write read & write read read
File Size mostly large small to large small to large mostly small
Concurrency high high low high

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TOP 5 LESSONS LEARNED
1.AIisaDataPipeline
2.Don’tThrowYourDataintoDataLake

14. © 2018 PURE STORAGE INC.14
DATA LAKE
OR DATA GRAVEYARD?
We see customers creating big data
graveyards, dumping everything into
HDFS [Hadoop Distributed File
System] and hoping to do something
with it down the road. But then they
just lose track of what’s there.
The main challenge is not creating a
data lake, but taking advantage of the
opportunities it presents.
“
”
PricewaterhouseCoopers
Technology Forecast, Issue 1, 2014

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MODERN ANALYTICS WITH OLD DATA LAKE
SPRAWLING, COMPLEX SILOS & SLOW PERFORMANCE
Each App Locked into Physical Silos
Redundant Data Copies in Silos
Fixed Compute to Storage in Silo
Built for Large, Sequential Data
Optimized for Batch, Not Real-Time
STATIC DATA LAKE
NO LONGER VIABLE
HDFS DATA LAKE
SILO
SILO
SILOSILOSILO

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TOP 5 LESSONS LEARNED
1.AIisaDataPipeline
2.Don’tThrowYourDataintoDataLake
3.CloudorNottoCloud?

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IT DEPENDS
WHERE YOU ARE ON YOUR AI JOURNEY
EXPLORATION PRODUCTION
NEED Start Immediately
Get New Products & Features to
Market Faster than Competition

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IT DEPENDS
WHERE YOU ARE ON YOUR AI JOURNEY
EXPLORATION PRODUCTION
NEED Start Immediately
Get New Products & Features to
Market Faster than Competition
DON’T NEED Bogged Down with Infrastructure
Bogged Down by Performance
& Cost Inefficiencies

19. © 2018 PURE STORAGE INC.19
IT DEPENDS
WHERE YOU ARE ON YOUR AI JOURNEY
EXPLORATION PRODUCTION
NEED Start Immediately
Get New Products & Features to
Market Faster than Competition
DON’T NEED Bogged Down with Infrastructure
Bogged Down by Performance
& Cost Inefficiencies
RECOMMENDATION Cloud On-Premises

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TOP 5 LESSONS LEARNED
1.AIisaDataPipeline
2.Don’tThrowYourDataintoDataLake
3.CloudorNottoCloud?
4.Lies,DamnLies,andBenchmarks

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BENCHMARKS DO NOT REFLECT REALITY
IMAGENET
REAL-WORLD AUTONOMOUS CAR
COMPANY
IMAGE SIZE 100-200KB 2-5MB
FILE SIZE 150MB
(Packed TFRecords)
2-5MB
MODE OF TESTING Synthetic (No I/O) Read from Storage

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AI TRAINING SYSTEM
GOAL IS TO KEEP THE GPUs 100% BUSY
decode scale
evaluate
forward-
propagation
update
back-propagation
GPUI/O CPU
FULL TRAINING
WORKFLOW
Setup #1: Synthetic Data
from System RAM into
GPUs
Setup #3: Real Image Data from FlashBlade
into DGX-1
BENCHMARK
SETUP
GPU ONLY I/O + CPU + GPU
Setup #2: Real Image Data
from System RAM Through
CPU + GPU
CPU + GPU

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NEAR-LINEAR SCALE DELIVERED
AIRI ENGINEERED FOR MAXIMUM PRODUCTIVITY AND OUT-OF-THE-BOX SCALE
DEEP LEARNING TRAINING- MULTI-NODE USING GPUDIRECT RDMA OVER ETHERNET
Comparing Synthetic Mode, Entire Data in DRAM, Entire Data in FlashBlade

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TOP 5 LESSONS LEARNED
1.AIisaDataPipeline
2.Don’tThrowYourDataintoDataLake
3.CloudorNottoCloud?
4.Lies,DamnLies,andBenchmarks
5.IdealDataPlatformisaDataHub

25. © 2018 PURE STORAGE INC.25
IDEAL PLATFORM FOR MODERN ERA
DYNAMIC DATA HUB ARCHITECTED FOR REAL-TIME & ELASTIC DATA
DATA PIPELINE
DATA HUB
“TUNED FOR EVERYTHING”
Small, Random to Large, Seq.
Architected for the Unknown
REAL-TIME
Low Latency Performance for
Instant Response
ALL-FLASH
Modern, Ultra-Fast
Technology
PARALLEL
No Serial Bottlenecks
for Max Throughput
ELASTIC
Grow Non-Disruptively
with More App Clusters
SIMPLE
Focus More on Insights,
Not Infrastructure

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NVIDIA® DGX-1™ | 4x DGX-1 Systems | 4 PFLOPS of DL Performance
PURE FLASHBLADE™ | 15x 17TB Blades | 1.5M IOPS
ARISTA | 2x 100Gb Ethernet Switches with RDMA
NVIDIA GPU CLOUD DEEP LEARNING STACK | NVIDIA Optimized Frameworks
AIRI SCALING TOOLKIT | Multi-node Training Made Simple
THE INDUSTRY’S FIRST
COMPLETE AI-READY INFRASTRUCTURE
HARDWARE
SOFTWARE

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AI & MODERN ANALYTICS
POWERING ANALYTICS FOR WORLD’S LARGEST PUBLIC HEDGE FUND
AI CLEAN & LABEL AI EXPLORE AI TRAIN
CPU Servers GPU Server GPU Servers
SPARK
CPU Servers CPU Servers
MONGO
Our quants want to test a model,
get the results, and then test
another one- all day long. So a
10-20X improvement in
performance is a game-changer
when it comes to creating a
time-to-market advantage for us.
Gary Collier, co-CTO, Man AHL
“
”

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ORCHESTRATION WITH OPENSHIFT
(KUBERNETES)
Monitoring
Load balancing
Scheduling
Resource allocation
OPENSHIFT + PURE PROVIDE RECIPE
FOR OPERATIONS AT SCALE

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TOP 5 LESSONS LEARNED
1.AIisaDataPipeline
2.Don’tThrowYourDataintoDataLake
3.CloudorNottoCloud?
4.Lies,DamnLies,andBenchmarks
5.IdealDataPlatformisaDataHub