In this session we show how engineers, analysts and scientists can develop, train, tune and deploy machine learning models via Amazon SageMaker, a docker-based, framework-agnostic orchestrator dedicated to machine learning with strong primitives for automation, monitoring and deployment. We will cover the following topics: - Amazon SageMaker architecture and key functionality - Scaling and deploying open source frameworks on Amazon SageMaker (sklearn, MXNet, Keras, TensorFlow, pyTorch, R) - Amazon SageMaker built-in Algorithm library: 18 state-of-the art algorithm covering broad use-cases, from computer vision to recommender systems - ML model deployment

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Amazon SageMaker
Develop, Train, Tune and Deploy Machine Learning Models
2019-04-24, D2SI Toulouse
Olivier Cruchant, ML Specialist SA, AWS

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

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Customer Running ML on AWS Today

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AWS ML Stack
P L A T F O R M S
A P P L I C A T I O N
S E R V I C E S
F R A M E W O R K S E T
I N F R A S T R U C T U R E
Amazon SageMaker
Vision
Rekognition Image
Rekognition Video
Amazon Textract
Speech
Amazon Polly
Amazon Transcribe
Language
Amazon Lex
Amazon Translate
Amazon Comprehend
Forecasting
Amazon Forecast
Personalization
Amazon
Personalize
DL AMI EC2 P3, C5, F1 GreengrassElastic Inference

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Build, Train and Deploy your Machine Learning Models
Amazon SageMaker
Models are abstracted into docker containers
Fast & accurate
data labeling
Built-in, high
performance
algorithms &
notebooks
BUILD
1
One-click
training and
tuning
TRAI N
Model tuning and
optimization
2
Fully managed
hosting with
auto-scaling and
elastic inference
One-click
Deployment of
model or Inference
Pipeline
DEPLOY
3

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Amazon SageMaker: TensorFlow workflow example
Develop on Notebook instance
with TensorFlow Jupyter kernel
BUILD
1
Train custom
code in
TensorFlow
container
TRAI N
Model tuning and
optimization
2
Deploy to auto-scaled
tensorflow-serving
container
DEPLOY
3
Stream training
data with
PipeModeDataset
Distributed training
with Horovod
Monitor with
TensorBoard

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Amazon SageMaker
Deployment & Inference
A. Deploy model to highly-available Amazon SageMaker HTTPS endpoint
B. Run Amazon SageMaker batch-transform job
C. On-device deployment (AWS Greengrass)

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Amazon SageMaker: algorithms
A. Train and deploy a built-in algorithm
B. Train and deploy your own code in a framework container
C. Train and deploy your own container
D. Bring a pre-trained model
E. Train and Deploy a Marketplace Algorithm
F. Deploy a Marketplace Model Package

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Amazon SageMaker: interface
A. Console
B. AWS Command Line Interface (CLI):
aws sagemaker create-endpoint
--endpoint-name <value>
--endpoint-config-name <value>
C. SageMaker Python SDK
model.deploy(initial_instance_count=1,
instance_type='ml.m4.xlarge')
D. Boto3 (python) SageMaker client
client.create_endpoint(EndpointName='string',
EndpointConfigName='string')
E. Other SDKs

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In summary:

11. Python SDK: Amazon Algorithm

12. Python SDK: Deep Learning Estimator

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Amazon SageMaker
Built-in algorithms
Supervised
Linear Learner
Factorization Machines
XGBoost
Image Classification
Object Detection
Sequence to Sequence
DeepAR Forecasting
K-Nearest Neighbors
BlazingText (classifier)
Object2Vec
Semantic Segmentation
Unsupervised
K-Means
Principal Component Analysis
Latent Dirichlet Allocation
Neural Topic Model
Random Cut Forest
BlazingText (word2vec)
IP Insights

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Object Detection
A. Single-Shot multi-box Detector (SSD): learns to locate and classify at the same time
B. Extract features with a convolutional base (VGG-16 and ResNet-50 available)
C. Transfer learning from ImageNet available
D. Example: aerial imagery analysis

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DeepAR
A. Forecasts scalar time series with RNN
B. Adapted to forecast families of similar time series (product sales, energy by household)
C. Learning set can include arbitrary categorical and time series features
D. Automatically derives features based on target frequency (eg day of month, day of year)
E. Automatically feeds lagged values from target time series
https://docs.aws.amazon.com/sagemaker/latest/dg/deepar_how-it-works.html

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BlazingText Word2Vec
A. Goal: produce low-dimension, dense semantic word
embeddings
B. CUDA implementation of word2vec that can be
distributed over multiple GPUs
C. Byproduct of a text classification problem (CBOW or
Skip-gram)
1
0
…
…
…
…
…
0
n1
n300
0
0
…
…
…
1
0
0
…
Apple Juice
wn1, 1
Voc size:
40k
wn1, 2
wn1, 40k
wn300, 1
wn300, 2
wn300, 40k
Word e1 … e300
Apple wn1, 1 … wn300, 1
… … … …
Zebra … … …
⟹
Simplified architecture. See original papers for more details
https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-
and-their-compositionality.pdf
https://arxiv.org/pdf/1301.3781.pdf
https://arxiv.org/pdf/1607.04606.pdf

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BlazingText Classifier
A. Extends fastText to work on GPU
B. Learns word embeddings that are averaged then linearly fit
C. Features include n-grams
D. Features are hashed to limit dimensionality
https://aws.amazon.com/fr/blogs/machine-learning/enhanced-text-classification-and-word-
vectors-using-amazon-sagemaker-blazingtext/

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Object2Vec
A. Generate predictions or embeddings
B. Use-cases: recommendation, similarity,
multi-label classification
C. Supports 2 types of inputs:
• Discrete tokens
• Sequences of discrete tokens

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Random Cut Forest
Algo:
1. Partition input space into random sample
2. For each sample:
i. Draw bounding box
ii. Random cut on a random feature (sampling proportional
to variance)
iii. Points alone in their bounding boxes become tree leaves
3. Outlier score inversely proportional to leaf depth
… …

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SageMaker Ground Truth

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SageMaker Neo
Compiler Runtime

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https://aws.amazon.com/sagemaker
http://d2l.ai/
Getting Started