AI: how to accelerate your business...
Transcript of AI: how to accelerate your business...
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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
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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
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What are the hurdles to leverage IA?
For respondents*, top three challenges
identified for implementing AI initiative are
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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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
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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
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CognitiveData Center Use Case
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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$/hour)
0,5M$
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> 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
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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
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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)
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Video Intelligence Use Case
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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
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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
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Deep Learning Real-time person reidentification
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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
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Conclusion
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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
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Thanks