The document discusses intelligent mobility and how machine learning can help improve transportation systems. It provides examples of how ML can be applied to roads, ports, railways and airports for tasks like license plate recognition, container tracking, flight delay prediction and predictive maintenance. The document also discusses how ML platforms can help companies build scalable predictive applications by standardizing workflows, integrating various data sources and empowering employees and domain experts to develop and use ML models.

1. Atakan Cetinsoy
VP of Predictive Applications
Machine Learning in the
Mobility Industry
@bigmlcom
#BigML
#IntelligentMobility
May 26, 2021
Intelligent Mobility

2. BigML,Inc
#IntelligentMobility
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Moving people and goods around in a more environmentally-
friendly and efficient way with the help of new and innovative
transport technologies that make journeys not only significantly
better but also more accessible globally.
Intelligent Mobility

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#IntelligentMobility
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Urbanization Climate Change Sharing Economy
Megatrends

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#IntelligentMobility
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ROADS
•License Plate Recognition
•Toll Sign Monitoring
•Dynamic Pricing
•Route Planning
4
PORTS
•Security / Surveillance
•Container Tracking and
Routing
•Ship Arrival Prediction
•Predictive Customs
Clearance
RAILWAYS
•Damage / Obstacle
Detection
•Smart Scheduling
•Speed Optimization
•Railcar Inspection
AIRPORTS
•Flight Delay Prediction
•Yield Optimization
•Passenger Security
•Predictive Maintenance
Intelligent Mobility Use Cases

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LEVEL4
LEVEL
3
LEVEL
2
Domain Experts
S e l e c t a n d
configure Machine
Learning workflows
Domain Experts
with ML
Experience
Design, implement
n e w M a c h i n e
Learning workflows
and models
Front-Line Employees
Machine learning is democratized down to an
intuitive UI/UX interface that visualizes predictions
and empowers front-line employees with the
ability to provide feedback to retrain the models.
IT Staff
Manage security,
assign resources to
different teams, add/
remove engineers,
provide permissions
for supervisors, etc.
LEVEL
1
Empowering Front-Line Employees

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•Companies with 5+ Machine Learning applications in production choose to license commercial
solutions at an even higher rate than ML Newbies.
Enhancing Existing Products and Services
Creating NEW Products and Revenue Streams
Top Objectives of AI/ML Initiatives
ML Platform — Build or Buy?
SOURCE: Deloitte Insights, State of AI in the Enterprise, 3rd Edition, 2020
Lowering Costs | Making Processes More Efficient

7. BigML,Inc
#IntelligentMobility
WORKFLOWS
DATA SOURCES
ALERTS
BIGML PLATFORM
Machine Learning platforms
provide a strong foundational layer
by packaging the best algorithms,
workflows, scripts in an easily
consumable manner Thus,
allowing Customers to scale
thousands of models across a
multitude of predictive use cases.
End users interface with easy-to-
digest alerts, dashboards and
recommendations.
SOURCE
SERVER
DATASET
SERVER
MODEL
SERVER
PREDICTION
SERVER
EVALUATION
SERVER
SAMPLE
SERVER
WHIZZML
SERVER
!7
Repeatable and programmable
workflows handle individual use
cases in a standardized manner.
Connectors for multiple data
sources make it easy to integrate
ML platforms with client IT systems.
Building Scalable Predictive Apps
•Faster time to market
•Minimized technical risk
•Ability to focus on business use cases — not infrastructure