Accelerating Machine Learning Adoption in the Automotive Industry
Accelerating ML Adoption
in the Automotive Industry
VP - Predictive Applications, BigML
Machine Learning in a Nutshell
Applied ML is primarily about finding patterns in business data,
that can be used to make useful business predictions
Automotive Industry Use Case Examples
Predictive Maintenance: Will this machine component fail?
Forecasting: How much of each vehicle model will we sell next quarter?
Supplier Risk: What will be the delivery performance per supplier?
Marketing: Which customers show affinity for shared mobility?
Finance: Is this transaction fraudulent?
Operations: Which manufacturing configurations are optimal to use?
Programming with Machine Learning
• Ultimately, Machine Learning is all about transforming data into models that
can be used to automate decision making.
ID COUNTRY CITY
xyz US SEA 5 22 1448 Yes
abc US PBI 8 9 2330 No
def US CLT 20 2 22296 Yes
nnx US MIA 4 19 32342 Yes
sbd US ANC 1 21 1144 Yes
fjm US MSP 5 8 1589 No
cft US MSP 6 7 1299 No
amt US CLT 14 2 1250 Yes
AA US DFW 1 13 1464 No
vgg US ATL 3 15 17471 Yes
Democratizing Machine Learning — Why Now?
Maturity of ML
The Economics of Machine Learning
• As the unit cost of predictions go down, many
facets of decision making will be automated via
• This means redesigning tasks with fewer human
predictions, but more human judgment.
The Machine Learning Revolution
+ Fast (i.e., milliseconds)
+ Better: Quantifiable/Near
Human-level Error Rates
Early Adopters — Google
• "Machine learning is a core, transformative way
by which we’re re-thinking how we’re doing
everything. We are thoughtfully applying it across
all our products, be it search, ads, YouTube, or
Play. And we're in early days, but you will see us
— in a systematic way — apply machine learning
in all these areas."
— Sundar Pichai, CEO
Machine Learning tools are
Machine Learning is intrinsically
Most businesses FAIL at Machine Learning :(
is going to revolutionize every industry and every organization BUT...
Building a Machine Learning Product
10.50 0.25 0.75
ML — Current State in the Automotive Industry
• Modest gains of AI/ML deployed at scale in 2018
among OEMs, suppliers, dealers from 7% to 10%
in one year.
• Companies applying more measured approach
in selecting use cases and projects.
• “Scale champions” (3+ at scale projects) better at
•Up or re-skilling workforce
•AI/ML governance process
•Yet 80% still mention AI/ML as a strategic
Automotive Vision 2030
• Slow (2%) growth in the traditional vehicle sales and related
• Automotive industry revenue to increase by $1.5T (30%) thanks to
new business models such as shared mobility and connectivity
• 10% of cars sold in 2030 will be shared vehicles adding to special
purpose ﬂeets and mobility-as-a-service solutions popular in dense
• Various ﬂavors of EVs will make up to 50% of vehicles!
• New competing ecosystems with more diverse players will emerge to
deliver a much more integrated customer experience.
SOURCE: McKinsey Global Institute
• Integrated software and data-driven insights
as the connective tissue.
ML for Automotive — Unfulfilled Potential
• Application of Machine Learning can boost pre-tax proﬁts of the industry by 5%
conservatively…and up to 16%.
• ML has a key role to play in the future of the automotive industry.
• Operational Efﬁciency
• Customer Satisfaction
• Direct Costs
• Customer Churn
• Time to Market
Machine Learning Accessibility Revolution
“ After years of hype around mysterious
neural networks and the PhD researchers
who design them, we’re entering an age in
which just about anyone can leverage the
power of intelligent algorithms to solve the
problems that matter to them. Ironically,
although breakthroughs get the headlines,
it’s accessibility that really changes the world.
That’s why, after such an eventful decade, a
lack of hype around machine learning may
be the most exciting development yet.”
— Andrew Moore, Google
Tale of Two Innovation Approaches
AutoML / Standard Workﬂows
• ML-literate Analysts, Developers, Subject Matter
Experts, and Decentralized Data Science Staff
• Centralized Data Science Staff and IT-led
Operationalization on Specialized Computing
Platforms and Open Source Tools
TOP DOWN / CENTRALIZED BOTTOM UP / GRASSROOTS
• Please visit us at the Thirdware
•see a live demo of the BigML
MLaaS platform and/or
•discuss your speciﬁc use case
Key to the Vault — ML Workflows & Automation
LEARNING OR TRAINING
SCORING OR PREDICTING
• Standardization of the end-to-end process
instills consistency, reliability, and collaboration.