ML6 is a machine learning company with over 70 years of combined experience. It has over 200 employees working across research, services, and engineering. The document discusses ML6's work in areas like computer vision, IoT, advanced process control, and others. It provides examples of ML6 using deep learning models for tasks like tumor detection, traffic congestion prediction, and optimizing industrial systems.

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ML6 AT A GLANCE
Not only knowledgeable about Machine Learning, but defining the future of it
70+ years of combined
Machine Learning Experience
20% of workforce fully dedicated
to 3 funded ML Research tracks
Premier Partner of Google Cloud
since 2014
Serving Clients across
40 different countries
Equipping 20% of BEL 20
companies with cutting-edge ML
4 Offices, located in Belgium, The
Netherlands and Germany

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WE WORK ACROSS THREE PILLARS
RESEARCH
A dedicated research division,
solely focused on innovating ML
algo’s enabling us to stay ahead
of competition
SERVICES
Passionate consultants to keep
the pulse on the market and set
up long lasting relationships with
our customers
ENGINEERING
Software engineers building our
ML building blocks and
maintaining our ML Marketplace
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IMAGE AND VIDEO

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HOND VS DWEIL

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TOO EASY?

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MUFFIN VS CHIHUAHUA

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A DEEP LEARNING MODEL IS TRAINED,
RESULTING IN TUMOR PROBABILITY MAP

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OTHERS

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OUR DISTRIBUTED FRAMEWORKS ALLOW
US TO RAPIDLY IMPLEMENT OTHERS

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DISRUPTING THE UTILITIES INDUSTRY
WITH COMPUTER VISION

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IOT &
ADVANCED PROCESS
CONTROL

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THE MODEL ACCURATELY PREDICTED
CONGESTIONS IN LONDON TRAFFIC

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PHYSICS SIMULATOR

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PHYSICS SIMULATOR

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A product by

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Energy
Raw
Material
People
System / Environment / Machine
Production
Quality
Consumption
Safety Days
Machine Settings
Manual raw material
fill settings
Rule based
maintenance
Rule based safety

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Energy
Raw
Material
People
System / Environment / Machine
Production
Quality
Consumption
Safety Days
Machine Learning
Optimisation
INLINE
TRAINING
Machine Settings
Manual raw material
fill settings
Rule based
maintenance
Rule based safety
Machine Learning
Automation

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27. ALPHA PARTNER USE CASE
Reducing giveaway of washing detergent
27
WHAT?
ECC.AI learned to continuously control a powder pouch filling process,
reducing the wastage from product giveaway due to variability of pouch
weights
HOW?
By continuously tuning the diaphragm and centrifugal system, whilst
taking into account product specific parameters and environment variables
WHERE?
At one of the largest FMCG production sites
TIME TO MVP?
3 months
INPUT NEEDED?
3 months’ historical data on input control, quality, weight and volume
ROI?
Improvement of 65% and growing
WITHOUT ECCAI
WITH ECCAI

28. ALPHA PARTNER USE CASE
Increasing pump life expectancy with closed loop control
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Breakdown
Probability
Optimal Settings
Current
Settings
Threshold
Predicted
Maintenance
Time
Breakdown
Probability
ECCAI CONTROL
ACTIVE
Current
Settings
Threshold
Time
WHAT?
ECC.AI learned to continuously control an air compressor pump,
increasing its life expectancy: the next step in predictive maintenance
HOW?
By monitoring real-time demand and temperature, and continuously
tuning fan speed and other control parameters to optimise the pump’s
output and thus life expectancy
WHERE?
At one of the largest pump manufacturers
TIME TO MVP?
6 months
INPUT NEEDED?
1 month historical data set at millisecond level
ROI?
Pending

29. ALPHA PARTNER USE CASE
Optimising Combined Heat and Power systems
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WHAT?
To optimise the steam generation kettle of a Combined Heat and Power
(CHP) system to increase overall energy efficiency.
HOW?
The water level of the steam kettle appeared to be difficult to control, but
using sensor data of temperature, pressure, water/steam flow and valve
positions etc, our algorithms learned the dynamics to accurately control
the water inlet, thus increasing its efficiency.
WHERE?
CHP power plant
TIME TO MVP?
3 months
INPUT NEEDED?
4 months’ sensor data of all relevant components (pressure, temperature,
valve positions, current control units etc)
ROI?
TBD

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The key lies in an “evolution not revolution” approach, where small pieces of process are automated
and optimized individually.
ECC.AI is best utilized in a dynamic environment with vast amounts of input, where today’s production
teams face repetitive tasks that could be replaced by cognitive machine learning for faster, more
optimal results.
Blueprint &
Roadmap
IT/OT
Connectivity
Integration
Real-time
AnalyticsDashboard &
Reporting
Connectivity
to Intelligent
ERP
Smart
Predictive
MES
Workforce Enablement
Largely Manual
and
Unconnected
Automated
Monitoring and
Connected
Intelligent and
Predictive AI Ecosystem
Integration
Dynamic and
Ecosystem-driven

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ABOUT THE SMART FACTORY
Suppliers &
Subcontractors
Suppliers &
Subcontractors
Customers
Customers
Smart Grid
Factory 1
Factory 2
Factory 3

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Karel Dumon (karel.dumon@ecc.ai)
Rebecca Brooke (rebecca.brooke@ecc.ai)
BUSINESS CASE
DATA SAMPLE
INFO @ ECC.AI

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THANKS
NICOLAS DERUYTTER
NICOLAS@ML6.EU