Supercharge Your Machine Learning Model with Amazon SageMaker In this session you will learn how to use Amazon SageMaker to build, train, test, and deploy a machine learning model. We will use a real life use case to share the simplicity of building and deploying ML models on Amazon SageMaker. Koorosh Lohrasbi, Solutions Architect, Amazon Web Services

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Koorosh Lohrasbi
Solutions Architect - AI/ML Focus, Amazon Web Services
Supercharge Your Machine Learning
Model With Amazon SageMaker

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Today’s Challenge

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Data
Build
Train
Deploy
A Typical Machine Learning Pipeline

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Data
Build
Train
Deploy
A Typical Machine Learning Pipeline

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Data
Build
Train
Deploy
A Typical Machine Learning Pipeline

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Data
Build
Train
Deploy
A Typical Machine Learning Pipeline

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Data
Build
Train
Deploy
A Typical Machine Learning Pipeline

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Output
Output
Challenge: Lack Of Training Data
Solution 1a: Transfer Learning

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Challenge: Lack Of Training Data
ImageNet
MNIST
CIFAR
Caltech256
Solution 1b: Public Datasets
Output
Solution 1a: Transfer Learning
Output

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Building A Model Development Environment
BUILD
Amazon EC2 DeepLearning
AMI
Local Machine
Notebooks

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Building Models - The New Way
BUILD
Amazon SageMaker Notebook
instance
Notebooks
Amazon EC2 DeepLearning
AMI

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Amazon SageMaker’s Notebook
Instance Demo

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Amazon SageMaker Algorithms And Frameworks
Selecting
Algorithms and
Frameworks
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorisation Machines
Linear Learner – Regression
DeepAR Forecasting
XGBoost
Latent Dirichlet Allocation
Image Classification
Seq2Seq
Linear Learner – Classification
ALGORITHMS
Apache MXNet
TensorFlow
Caffe2, CNTK,
PyTorch, Torch
FRAMEWORKS
Notebooks
BUILD

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Building Algorithms
Demo

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Training Models – The Painful Way
TRAIN
Model
Training
IAMAWSKMS
Amazon
CloudWatch
Container farm Spot Fleets
EC2
Cluster
AWS Batch Amazon S3
Amazon
EFS
Amazon
EBS
AWS
CloudFormation
Amazon
VPC
AMIAWSCodeStar
On-premises server farms

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Training Models - Using Amazon SageMaker
TRAIN
Model
Training
One-click Training and HPO
IAMAWSKMS
Amazon
CloudWatch
Container farm Spot Fleets
EC2
Cluster
AWS Batch Amazon S3
Amazon
EFS
Amazon
EBS
AWS
CloudFormation
Amazon
VPC
AMIAWSCodeStar
On-premises server farms

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Training on Amazon SageMaker
Demo

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Deploy Models - The Painful Way
Deploy
Model
Deployment
IAM
Amazon
CloudWatch
Container farmEC2 Cluster Amazon S3
Amazon
EFS
Amazon
EBS
AWS
CloudFormation
On-premises Prod
environmentELB
Elastic
Beanstalk Lambda APIGateway
Amazon WAF AWSKMS

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Deploy Models - Using Amazon SageMaker
Deploy
Model
Deployment
One-click Deploy and Scaling
IAM
Amazon
CloudWatch
Container farmEC2 Cluster Amazon S3
Amazon
EFS
Amazon
EBS
AWS
CloudFormation
On-premises Prod
environmentELB
Elastic
Beanstalk Lambda APIGateway
Amazon WAF AWSKMS

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Deploying on Amazon SageMaker
Demo

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Build Using SageMaker
BUILD
Pre-built
notebooks for
common
problems
Built-in, High
performance
Algorithms

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Train Using SageMaker
BUILD
Pre-built
notebooks for
common
problems
Built-in, High
performance
Algorithms
TRAIN
One-click
Training
Hyperparameter
Optimisation

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Deploy Using SageMaker
DEPLOY
Fully managed
hosting with
Auto-scaling
One-click
Deployment
One-click
Deployment
BUILD
Pre-built
notebooks for
common
problems
Built-in, High
performance
Algorithms
TRAIN
One-click
Training
Hyperparameter
Optimisation

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Improving Results
More Data

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Improving Results
More Data
Hyperparameter
Optimisation
HPO

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Improving Results
More Data
Hyperparameter
Optimisation
HPO A/B Testing
A B
Control Variation

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Machine Vision Use Cases

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Medical Imaging

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Real-Time Logistics Analysis

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Image Based Material And Price Evaluation

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Digital Content Presentation

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Instead of trying to produce a programme to
simulate the adult mind, why not rather try to
produce one which simulates the child’s?
- Alan Turing 1950
“
”

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Thank You