AI: how to accelerate your business...

AI: how to accelerate your business projects

Jérôme StollerDistinguished Expert, VP, Head of AI Incubator, Atos

Cedric BourrassetSenior Expert, Atos Codex AI Suite Product Manager

Analytics

of all business analytics

software will include

prescriptive analytics built

on cognitive computing

functionality by 2020*

Smart machines

of new enterprise

applications will include

smart machine

technologies**

Cognitive

of all consumers

will interact with

services based on

cognitive computing on

a regular basis by

2018*

Artificial Intelligence apps gain momentum

What are the hurdles to leverage IA?

For respondents*, top three challenges

identified for implementing AI initiative are

Challenges when starting your AI journey

Data AI Platform Data Science

Enterprises are facingthe most acute skillsshortage

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Challenges when starting your AI journey

Data AI Platform Data Science

Enterprises are facingthe most acute skillsshortage

Business goals

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Challenges when starting your AI journey

Business goals

Atos AI Labs to find your AI business value

Define

your AI journey in just 2-days

Design

the AI project to start with

Discover

what AI can do for you

AI learning AI projectsAI journey

& platforms

Challenges when starting your AI journey

Data Business goals

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People

Who fills the roles

“The Data”

What it is, where its used

Business Processes & Controls

The process of “Managing”

Governance

Strategy, roles, responsibilities

Data Analytics Platform & Tools

Supporting the Pyramid

Technology▶ Where stored, how shared, how changed,

how recovered, how secured ▶ Unified Data Architecture with Data Lake,

Anonymization, Metadata repository

Governance is the starting point▶ The overall Data framework▶ Strategy, Objectives▶ Operating Model

Data Organisation▶ The data decision makers, stewards, scientists, engineers▶ Business Users

Data Landscape▶ Data Standards▶ Data Cleaning▶ Analytics Pipelines

Data Processes▶ DataOps Development & Maintenance▶ Manage data standards, maintain data,

ensure data quality, data cleansing

Setting a trusted data approach

Codex DataLake Engine in a nutshell

— Control & visibility of all data

— Data security & sovereignty

— Organizations spend less time installing, tuning, data operating, troubleshooting, upgrading,

— BullSequana S appliance

— Cloudera certified

Bringing outstanding features

Data Ingestion, preparation

Data cleansing, blending

Data anonymization ,lineage

Disaster recovery

Data purge

VIR

TU

ALIZ

ATIO

N Scalable1

2Fully virtualized, redundant

3 High Availability

4Single Point Of Contact

Cloudera certified

Challenges when starting your AI journey

Data AI PlatformBusiness goals

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Atos technologies on AI

EnterpriseComputing

3Y roadmap with a meeting point

on Quantum roadmap

HighPerformanceComputing

EdgeComputing

Next gen memory & node controler

New components targeting Exascale

New use cases

2017 2018 2019 2020

BullSequana S(2 to 32 CPU + 2 to 32 GPU)

Machine Intelligence(with Siemens)

BullSequana X(GPU blades)

Google Alliance(ML Engine & APIs in the Cloud)

Edge Computer Prototype(embedded computing)

EPI(European Processor Initiative with Vector acceleration)

EPI(European Processor Initiative with Deep Learning acceleration)

Data Lake Appliance(also used in Prescriptive SOC)

An AI software suite, leveraging machine learning and deep learning capabilities to easily build and deploy artificial

intelligence applications across any infrastructure

Fast track to Artificial Intelligence

End-to-end, fast development of complex enterprise use

cases

Enable to easily combine ML, DL, analytics and data management to deliver complex and accurate use cases

Applications are infrastructure-agnostic

Enable applications to run on HPC, enterprise and Edge servers, on clouds and on-premises

HPC business can deliver NG applications

From precision medicine, to imaging diagnosis, driver assist, autonomous maintenance, …

Studio Forge Orchestrator ML Engine

Codex AI suite differentiators

Codex AI Suite at a glance

STUDIOcognitive application development self-service

FORGEcommon workplace where to store,

share, retrieve and update

ComponentsBlueprintsDL frameworks

Data setsTrained models

FastMLENGINE

Application Builder FastML Engine

ORCHESTRATORfast application deployment on multiple environments

High Level API

The Codex AI Suite leverages our partners to accelerate AI solutions

Codex AI Studio is the common workbench to develop use cases

Deep Learning engineGoogle ML services Partners

(components, algorithms, applications, models)

Orchestrator

Enterprise, Edge, HPC, On-premise, Cloud

Forge

Atos AI-Edge Server - Q1 2019

> Designed to operate outside of a Datacenter

– Factory/Shop floor, Airport halls, Ships, ..

– DIN railmount option

> Very powerful CPU and GPU capabilities for an Edge class server

– designed to provided exceptional Machine Learning inference performance

> Can optionally be mounted in a standard 2U Rack

Challenges when starting your AI journey

Data AI Platform Data Science

Enterprises are facingthe most acute skillsshortage

Business goals

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ATOS Research program on AI

Free University of Brussels- Scalable fraud detection techniques in big data context- Time Series

Jean Monnet University / Hubert Curien - St Etienne- Online learning and boosting methods- Massive Deep Learning topology

INSA of Lyon- Linked Data for Prescriptive Analytics- Feature Selection- Data Value assessment

University of Milano- Expert rules ranking methods

University of Passau- Data Value Assessment- Certificate Program- Deep Learning on Data Streams

Catholic University of Louvain- Graph mining for fraud detection

ENS – Saclay- Advanced Machine Learning- Algorithm development- IPOL

CEA List – Saclay- Advanced Machine Learning FMK- Machine Learning expertise- High performance hardware

Brussels

AachenFrankfurt

Paris

Lille

Lyon

St Etienne

Milano

MunichRennes

CognitiveData Center Use Case

Each minute of partial or global outage in a Data Center costs 9k$ in average.

Source : Ponemon Institute, Cost of Data Center Outages, January 2016

An hour of unplannedoutage costs

(up to a max of1M$/hour)

0,5M$

> Prediction of incidents

> Identification of the Root Cause analysis in real time

Cognitive DataCenter: Outcomes

Incidents not predicted and confirmed: 0%

Predicted and unconfirmed incidents: 4%

Predicted and Confirmed Incidents: 96%

Results Example x2 speedup in Time to Solve

x6 speedup in RCA

Cognitive Datacenter: operating mode

PREDICTIVE MODULE

Indicators

Prediction QualityKPI

ML A

LG

OR

ITH

MS

Incident detection

Incidentpattern & rules

IF YES ALERTING

MODEL RE-TRAINING If KO

BATCH MODULE REAL TIME MODULE

An insight on our ML algorithms

– Preprocessing (Encoding, standardization, missing value)

– Automatic outlier detection

– Data behaviors (8 most used distribution probabilities law)

– Time series

– Regression models and interpolation

– Random forest

– Genetic algorithm

– Bayesian network

REPRESENTATION (From analysis to prediction)

– Precision

– Quadratic error

– Later probability

– Cost / Margin

– Entropy

– K-L divergence

EVALUATION (Prediction Quality)

– Combinatorial optimization

– Convex optimization

– Constraint optimization

OPTIMISATION (From simple prediction to the best prediction)

Video Intelligence Use Case

AI powered Video Security & Digital Signage

Image credit https://www.slideshare.net/secret/dNQDdao7n6uY2A

Video Security

- Crowd movement- Scenes of violence- Abandoned objects- Identification of blacklisted

person- Identification of car plates P

EO

PLE S

AFETY

Digital Signage

- Optimize commercial spaces- Dynamic advertisement- Efficient signage- Passenger traffic flows well- Dynamic and personalized

passenger information

PEO

PLE

SA

TIS

FA

CTIO

N

UNIQUE PLATFORM

From pixel to semantics

Scene understanding, object/humanrecognition, reidentification and tracking across a camera network, search in videos, crowd analysis, action detection etc

Scalable & embeddable

Real-time perception, save network bandwidth & reduce data center overloading, hierarchical control loop

Enterprise class components to extract value from images & videos

Deep Learning Real-time person reidentification

Segmentation

1000 classes: ~50 categories +attributes

Adding new class requires ~ 100 to 1000 images to retrain the model

Density estimation GAN

Results with SaCNNInference time: ~69ms on P100MAE : ~17.6 (Avg # of person : 123.6)

Managed properties: gender, age, hair color, bearb, etc

Underlying Deep Learning technologies

Conclusion

Challenges when starting your AI journey

Data AI Platform Data Science

Enterprises are facingthe most acute skillsshortage

Business goals

1 0 0 1 0 1 1 1 0 0 1 0 0 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1

Thanks