This session is intended for mobile developers who want an understanding of machine learning and how to use it in a mobile application without understanding the complexities of algorithms and how they train models and learn. We'll briefly define AI and machine learning, explain why machine learning is so hot, pinpoint the parts of machine learning most relevant to mobile development, and provide tips for getting started. Level: Beginner Speaker: Dennis Hills - Developer Advocate, AWS Mobile Applications

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Introduction to Machine
Learning on Mobile
Dennis Hills
Mobile Developer Advocate
Pop-up Loft

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What is AI?
Artificial Intelligence (AI) is a broad term for applying ANY technique
that enables computers to mimic human intelligence, using logic, if-
then rules, decision trees, and machine learning (including deep
learning). – think chatbots, robots, and KITT from Knight Rider!
Inventing entirely
new customer
experiences
Drones Voice driven
interactions

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What is Machine Learning?
A subset of AI: Machine learning
(ML) is a set of methods that can
automatically detect patterns in
data, and then use the uncovered
patterns to predict future data, or to
perform other kinds of decision
making under uncertainty.
Personalized
recommendations
Fulfillment
automation and
inventory
management

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More definitions…
Machine Learning is all about using data to answer
questions. First, data (e.g., images, text, or voice) is
provided along with answers (labels) to that data. Then the
computer (model) is trained on this data so it can “learn”
and later make predictions (aka inference) on the mobile
device.

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Important limitations for mobile
In general… mobile apps use pre-trained models to make
predictions. These models are first trained outside of the
app—typically in the cloud—and then brought into the app
to accomplish the task you desire.

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Why all the hype?!
Several developments in the world of ML
are creating an exciting playing field for
mobile developers:

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What’s Changed?
Advances in neural networks (algorithms) have
dramatically improved accuracy in recognizing
images and speech. Accuracy rates matter. So,
what seemed like science fiction not so long ago is
here today.
1

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What’s Changed?
Advances in cloud computing have greatly
reduced the time it takes to train these models. I’m
talking weeks to hours! That means fewer
resources, reduced capital expenses, and faster to
market.
2

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What’s Changed?
There’s also been a flood of third-party API-driven
machine learning services hitting the market that
do a lot of the heavy lifting for you. Build your own
or let someone else do it.
3

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Three Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning

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Supervised Learning
Supervised learning is a system of all labeled
data and a predictive model. We use labeled data
to train a “model” and then use that “model” to make
predictions (inference) on new unlabeled data. The
trained model is the core of our discussion when it
comes to machine learning on a mobile device.

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Getting Started
So, we now understand machine learning, why it’s
being used, and what problems it can solve, but how
do I start playing with it as a mobile developer?

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iOS => Core ML
Android =>
TensorFlow Lite

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Now, you may be asking…why not TensorFlow
Lite on Android and iOS?!?!
Answer: Use the best tool for the job

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Best of Both Worlds When it Comes to Prediction
The ML model is really the core of ML and you are
simply interfacing with the same trained model on the
device, but using the best framework for each platform.

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Imagine a little baby sitting between the two icons . . . that baby is the ML Model
they both have in common.

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Thank you
Get Started:
aws.amazon.com/mobile
AWS Mobile Twitter:@AWSforMobile
Dennis Hills on Twitter: @dmennis