Intro to SageMaker

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
Soji Adeshina, Amazon AI
An Introduction to SageMaker

Roadmap
1. What can we do with Machine Learning?
2. What are the pain points?
3. How does SageMaker mitigate those pain
points and provide leverage?
4. Walk-throughs with SageMaker

1. What can we do with
Machine Learning

We can classify things
https://www.slideshare.net/butest/machine-learning-3860079

We can try to identify things in pictures
http://www.robots.ox.ac.uk/~vgg/projects/seebibyte/demo.html
https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-
learning-495ef744fab

We can try to find correlations between things
http://upibi.org/penn-team-uses-machine-learning-and-twitter-to-
predict-heart-disease-risk/

We can assign topics to articles
https://medium.com/@connectwithghosh/topic-modelling-with-latent-dirichlet-allocation-lda-in-pyspark-
2cb3ebd5678e

Put machine learning in the hands of every developer and data
scientist
ML @ Amazon: Our Mission

2. What are the pain points of
ML?

Let’s Review: Machine Learning to Production
DeployTrain &Tune
Data +
Data Processing

Machine Learning Pain Points
• Manually setting up the training workflow and managing
is a project in itself.
• Keeping track of training experiments and tuning
hyperparameters is hard
• Dealing with distributed training and managing your
training cluster is often cumbersome
• Deploying to production means hosting a web service,
writing software to handle incoming requests

3. How does AWS and
SageMaker mitigate those pain
points and provide leverage?

FRAMEWORKS AND INTERFACES
AW S DEEP LEARNING AMI
Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano
PLATFORM SERVICES
VISI ON
AWS DeepLensAmazon SageMaker
LANGUA G E
A P P L I C A T I O N S E R V I C E S
Amazon
Rekognition
Amazon
Polly
Amazon
Lex
Amazon
Rekognition
Video
Amazon Transcribe Amazon Translate
Amazon
Comprehend
Alexa for Business
VR/IR Amazon Sumerian
Amazon Kinesis
Video Streams
AWS ML Stack

SageMaker Overview
A fully managed servicethat enables data scientists and developers to quickly and easily build
machine-learning based models into production smart applications.

SageMaker Overview
BUILD
Collect & prepare training data
Data labeling & pre-built notebooks
for common problems
Choose & optimize your ML
algorithm
Model & algorithm marketplace &
built-in, high-performance
algorithms
TRAIN
Setup & manage environments for
training
One-click training on the highest
performing infrastructure
Train & tune model
Train once, run anywhere & model
optimization
DEPLOY
Deploy model in production
One-click deployment
Scale & manage the production
environment
Fully managed with auto-scaling for
75% less

SageMaker Python SDK
Training
Environment
Estimator
Prediction
Environment
Predictor
Training Data
Trained Model
Car?
.fit( )
.deploy( )
.predict( )
train.py
endpoint
File by Iconstock from the Noun Project

• Linear Learner
• Image Classification Algorithm, Object Detection Algorithm
• Factorization Machines, Principal Component Analysis (PCA)
• K-Means Algorithm
• K-Nearest Neighbors
• Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM)
• Random Cut Forest, XGBoost Algorithm
• BlazingText, Sequence2Sequence
• DeepAR Forecasting
SageMaker Built-in Algorithms

4. Walk-throughs with
SageMaker

Setting up Sagemaker Notebook Instance

Thanks!
And don’t forget to check out:
https://medium.com/apache-mxnet
https://github.com/awslabs/amazon-sagemaker-examples

Intro to SageMaker

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