Machine learning techniques are being applied to every industry, leveraging an increasing amount of data and ever faster compute. But that doesn’t mean machine learning techniques are a perfect fit for every situation (yet). So how can a startup harness machine learning for its own set of unique problems and solutions, and does it require a warehouse filled with PhDs to pull it off?

1. Machine Learning for Builders: Tools,
Trends, and Truths
Rob De Feo Shelley Trout
With: Moderated by:
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The Modern Tech Stack
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4. About Rob De Feo
Rob De Feo is startup advocate at Amazon Web Services and host of Startup Engineering Podcast spending his
time helping startups in Europe any beyond. He mentors startups CTO’s and founders as they begin their journey,
to helping large scale startup leverage technology as they build and scale their businesses. He has helped many of
Europe’s largest startups and startup institutions, including Techstars, Deliveroo, Monzo, Skyscanner, and Starling
Bank to scale their platforms and meet global demand. Rob is now focused on working with the stage startup
communities across Europe. Prior to Amazon, Rob was the founder of AI startup Jemboo, and was technical lead
of Expedia’s Innovation division, building one of the earliest industry chatbots.
About Shelley Trout
Shelley leads webinar production at Aggregage, the publisher of more than 40 B2B publications. She has successfully
produced hundreds of webinars featuring thought leaders, across a range of industries, and delivering diverse and rich
content and tens of thousands of targeted leads to webinar sponsors. Shelley is a published author and writer, who
previously worked as a director and producer on the West End while studying Theater in London. After moving to the
U.S., she majored in Anthropology & Archaeology at UC Berkeley, worked at the Center of Digital Archaeology, and
wrote her thesis on digital representations of people and their stories.
The Modern Tech Stack
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5. Rob De Feo
Startup Advocate
@robdefeo
StartupEngineering.co
Machine Learning for Builders:
Tools, Trends, and Truths

6. Character Mode GUI Web Mobile
1970s 1980s 1990s 2000s

7. The reach of ML is growing

8. AmazonGo PrimeAir AWS AI

9. Customer Running ML on AWS Today

10. Our mission at AWS
Put machine learning in the
hands of every developer

11. What is it?
Artificial intelligence
Subset of AI that uses machines to search for patterns in data
to build logic models automatically
Subset of ML composed of deeply multi-layered neural networks that
perform tasks like speech and image recognition

12. Data matters
at any scale
Embrace ML of
every size and shape
Developers
love speed
T h r e e t r e n d s w e ’ r e s e e i n g a s c u s t o m e r s
a d o p t M L

13. T h r e e t r e n d s w e ’ r e s e e i n g a s c u s t o m e r s
a d o p t M L
Data matters
at any scale
Embrace ML of
every size and shape
Developers
love speed

14. New data is
created
in the cloud
Existing data
migrating as
quickly as
possible
Encryption,
auditing &
compliance
Analysis
through
a data lake
Human annotation
through the crowd
I t ’ s n e v e r b e e n e a s i e r o r c h e a p e r t o
c o l l e c t d a t a a t a n y s c a l e

15. T h r e e t r e n d s w e ’ r e s e e i n g a s c u s t o m e r s
a d o p t M L
Data matters
at any scale
Embrace ML of
every size and shape
Developers
love speed

16. Frameworks Interfaces
Infrastructure
A m a z o n
E C 2 P 3
& P 3 D N
A m a z o n
E C 2 C 5
F P G A s A W S
G r e e n g r a s s
A m a z o n
E l a s t i c
I n f e r e n c e
A m a z o n
I n f e r e n t i a
M L F r a m e w o r k s & I n f r a s t r u c t u r e

17. Gro und Truth
Algo rithms
No tebo o ks
Ma rketpla ce
Mo del tra ining & tuning
Priva te link
Auto - sca ling
M L S e r v i c e s
HPO
Optimiza tion (Neo )
Reinfo rcement lea rning
Build Train Deploy
Deplo yment & ho sting
Virtua l Priva te C lo ud
E la stic Inference
Amazon
SageMaker

18. A m a z o n
R e k o g n i t i o n
I m a g e
A m a z o n
P o l l y
A m a z o n
T r a n s c r i b e
A m a z o n
T r a n s l a t e
A m a z o n
C o m p r e h e n d
A m a z o n
L e x
A m a z o n
R e k o g n i t i o n
V i d e o
Vision
Speech Language Chatbots
A m a z o n
F o r e c a s t
Forecasting
A m a z o n
T e x t r a c t
A m a z o n
P e r s o n a l i z e
Recommendations
A P I S e r v i c e s

19. T h r e e t r e n d s w e ’ r e s e e i n g a s c u s t o m e r s
a d o p t M L
Data matters
at any scale
Embrace ML of
every size and shape
Developers
love speed

20. Remove
undifferentiated
heavy lifting of ML
Iterate, iterate,
iterate!
Training speed Inference speed
D e v e l o p e r s l o v e s p e e d i n A I
e x p e r i m e n t a t i o n

21. Our Promise To Customers: Smarter Alexa
NaturalSelf-learningCompetency Context-aware

22. TIME
COMPETENCY
Capabilities

23. Error rate
COMPETENCY
Capabilities
TIME

24. Context-aware

25. “What should I make
for dinner?”
“What is the
weather like in
Tahoe this
weekend?
“Suggest a spring
vacation
destination.”

27. Self-learning

28. Domain X Domain Y
Transfer Learning

29. Reinforcement Learning
Model
Reward function
RL Environment
Training Data

30. Natural

31. WEATHER REPORTS
TRAFFIC
9AM
MUSIC
LUNCH DELIVERY
12PM
HOMEWORK HELP
RECIPE RESEARCH
3PM
COOKING TIMERS
MUSIC
6PM

32. Ambient Computing Powered By AI

33. THE GOOD WE CAN DO WITH AI

35. 75 BILLION connected devices
BY 2025…

36. “Where next?”

37. Find out more
Rob De Feo - @robdefeo
StartupEngineering.co
ml.aws

38. The Modern Tech Stack
Webinar Series
Q&A
Shelley Trout
With: Moderated by:
Startup Advocate, Amazon Web Services
Linkedin page: /in/robdefeo/
Twitter ID: @robdefeo
Podcast: startupengineering.co/
Rob De Feo
Managing Editor, Aggregage
Linkedin page: /in/shelleytrout/
Twitter ID: @ctouniverse
Email: shelley@aggregage.com
Website: ctouniverse.com
https://www.ctouniverse.com/webinar-series/10744/the-modern-tech-stack