Speaker: Sara Mitchell, FS Specialist Solutions Architect
, AWS There is a myth that says you can only do Machine Learning if you have a PhD in Data Science. This session will show you how to ‘fake it’ as a data scientist using pre-built APIs for image and language processing, and then go into more depth about what the Machine Learning process is, the different kinds of algorithms, and how to train your own models on Amazon SageMaker, with no PhD in sight.

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Artificial Intelligence and Machine
Learning for Developers
Sara Mitchell | October 2018
Faking it as a Data Scientist
Using AWS AI services and Amazon Sagemaker

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What is AI, ML and DL?
https://www.geospatialworld.net/blogs/difference-between-ai%EF%BB%BF-machine-learning-and-deep-learning/

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Amazon Rekognition
Object and Scene
Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Celebrity
Recognition
Image
Moderation
Extract rich metadata from visual content

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Maple
Villa
Plant
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard

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Demographic Data
Facial Landmarks
Sentiment Expressed
Image Quality
Brightness: 25.84
Sharpness: 160
General Attributes

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Facial Recognition
Find similar faces in a large collection of images
SearchFacesByImage
Search
Index
Collection

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© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

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Amazon Comprehend
STORM
WORLD SERIES
STOCK MARKET
WASHINGTON
LIBRARY OF
NEWS ARTICLES
Amazon
Comprehend
Di scover i nsi ght s and rel at i onshi ps i n t ext

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Amazon Comprehend

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Know what your customers are asking
Deri ve i nsi ght i nt o every phone cal l
Amazon
Comprehend
Incoming
customer
call
Amazon
Transcribe
Amazon Connect Call
Recordings
Amazon
Quicksight
Amazon Machine
Learning
Entities
Key Phrases
Language
SentimentS3
S3
Lambda

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Polly and SSML
'Can you make your voices sound like an auctioneer?'
<speak><prosody rate='+60%'>I’m at 500 and I want
550<prosody volume='x-loud'>550</prosody></prosody>
<prosody rate='+60%'>bid on 550 I’m at 500 would you go
550 550 for the gentleman in the corner</prosody> <prosody
rate="+90%">A big black bug bit a big black bear a big
black bug bit a big black bear</prosody> Do we get 600?
<prosody rate='+90%'>A big black bug bit a big black
bear</prosody><prosody rate='+60%'>We got 600 for the
whole herd</prosody><prosody rate='default' volume='x-
loud'>Sold <prosody rate='+60%'>for
600.</prosody></prosody></speak>

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So you’ve mastered the API services …
But now you want to understand how ML Models work
?

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The Machine Learning process
Fetch data
Clean &
format data
Prepare &
transform
data
Train model
Evaluate
model
Integrate
with prod
Monitor /
debug /
refresh
Data wrangling
• Set up and
manage data
workbench
• Get data to
workbench
securely
Experimentation
• Set up and
manage clusters
• Scale / distribute
ML algorithms
Deployment
• Set up and
manage inference
clusters
• Manage and auto
scale inference
APIs
• Testing,
versioning, and
monitoring

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Prepare your data
• Use hosted Jupyter notebook
• Curl to copy the dataset
• Python to process the data

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Machine Learning Models
Regression
Classification Clustering
Association
Supervised Learning Unsupervised Learning
Known Categories
Medical Imaging
Cat or Dog or Fish
Continuous Target
Variable
What will the temperature
be tomorrow?
Target not known
Customer segmentation
Target not known
Market basket analysis

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Machine Learning Models
Classification
Reinforcement Learning
Known Categories
Optimised marketing
Target not known
Robotics
Iterative

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Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

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Amazon Sagemaker
Highly-optimized
machine learning
algorithms
BuildPre-built
notebook
instances

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Amazon Sagemaker
Highly-optimized
machine learning
algorithms
One-click training
for ML, DL, and
custom algorithms
BuildPre-built
notebook
instances
Easier training with
hyperparameter
optimization
Train

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Amazon Sagemaker
One-click training
for ML, DL, and
custom algorithms
Easier training with
hyperparameter
optimization
Highly-optimized
machine learning
algorithms
Deployment
without
engineering effort
Fully-managed
hosting at scale
BuildPre-built
notebook
instances
Deploy
Train

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Training a model
Use an algorithm together with test data to create a
model
• AWS pre-built algorithms
• You provide an algorithm
Automatic Parameter Tuning
• Hyperparameter optimisation
• Tunes training jobs for values in a range
• Uses machine learning to determine the combination of values

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Sagemaker provides many pre-built algorithms
How do you chose?
General Purpose Algorithms
• Linear Learner and XGBoost - discrete classification problems
• K-Means Algorithm – clustering, unknown target
Algorithms for specific use cases
• Image Classification Algorithm - It uses example data with answers (therefore a supervised
algorithm).
• Sequence2Sequence - This supervised algorithm is used for neural machine translation.
• Latent Dirichlet Allocation (LDA) Determines topics in a set of documents. It is an unsupervised
algorithm.
• Neural Topic Model (NTM) - Another unsupervised technique for determining topics in a set of
documents, using a neural network approach.
And many more …

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I
ML Hosting Service
Amazon ECR
30 50
10 10
ProductionVariant
Model Artifacts
Inference Image
Model versions
Versions of the same
inference code saved in
inference containers.
Prod is the primary
one, 50% of the traffic
must be served there!
Create an
EndpointConfiguration
from one or many
ProductionVariant(s)EndpointConfiguration
Amazon SageMaker
Easy Model Deployment to Amazon SageMaker
InstanceType: c3.4xlarge
InitialInstanceCount: 3
ModelName: prod
VariantName: primary
InitialVariantWeight: 50

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SageMaker
Notebooks
Training
Algorithm
SageMaker
Training
Amazon ECR
Code Commit
Code Pipeline
SageMaker
Hosting
Coco dataset
AWS
Lambda
API
Gateway
SageMaker Sample End-to-End Architecture: Style Transfer
Build
Train
Deploy
static website hosted on S3
Inference requests
Amazon S3
Amazon
Cloudfront
Web assets on
Cloudfront

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Amazon EC2 P3 Instances
The f ast est , most powerf ul GPU i nst ances i n t he cl oud
• Purpose built for AI/ML
• Massive amounts of parallel
processing power
• Enables data scientists to
cut down ML training times
from weeks to days
• Supported by a wide range
of AI/ML packages
AWS Deep Learning AMI

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The Amazon AI/ML Stack
PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Rekognition Transcribe Translate Polly Comprehend Lex
INFRASTRUCTURE
CPU IoT & EdgeGPU (P3) Mobile
Amazon Machine LearningMacie GuardDuty

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Further Resources
Today
• 1.30 - 21st Century Ways of Engaging with Your Customers
• 2.10 - Fraud Detection and Prevention
• 4.30 - The Machine Learning Factory
AWS Machine Learning Blog
• https://aws.amazon.com/blogs/machine-learning/
Amazon Sagemaker
• https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html
Free Training
• https://www.coursera.org/learn/machine-learning

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Amazon SageMaker
aws.amazon.com/sagemaker
aws.amazon.com/machine-learning
End-to-End Managed ML Platform