building and running your first machine learning...
TRANSCRIPT
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Constantin Gonzalez
Principal Solutions Architect, Amazon Web Services
Building and running your first Machine
Learning application on Amazon
SageMaker
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
What you’ll get out of this session
• An overview of the Machine Learning (ML) process
• An overview of Amazon SageMaker
• Examples for:
• Using Jupyter Notebooks
• Feature extraction and data preparation in Python
• ML algorithms available in Amazon SageMaker
• Building, training and deploying ML models
• Hands-on
• Opportunities for Q&A
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Overview
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Long history of ML at Amazon
Personalized
recommendations
Inventing
entirely new
customer
experiences
Fulfillment
automation and
inventory
management
Drones Voice-driven
interactions
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning at AWS
Our mission:
Put machine learning in the hands of every developer and
data scientist
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Amazon Machine Learning stack
Frameworks & Infrastructure
Platform Services
Application Services
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Bottom layer: frameworks & interfaces
NVIDIA
Tesla V100 GPUs
P3
AWS Deep Learning AMI
5,120 tensor cores
128 GB of memory
1 petaflop of compute
NVLink 2.0
~14X faster than P2
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Amazon Machine Learning stack
Frameworks & Interfaces
Platform Services
Application Services
Caffe2 CNTKApache
MXNetPyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon SageMaker
End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Build, train, and deploy machine learning models at scale
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Amazon Machine Learning stack
Frameworks & Infrastructure
Platform Services
Application Services
Caffe2 CNTKApache
MXNetPyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon ML application services
Vision Speech Language
Amazon Rekognition
Amazon Rekognition
Video
Amazon Polly
Amazon Transcribe
Amazon Lex
Amazon Translate
Amazon Comprehend
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
The Amazon Machine Learning stack
Frameworks & Infrastructure
Platform Services
Application Services
Caffe2 CNTKApache
MXNetPyTorch TensorFlow Torch Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
Amazon
RekognitionAmazon Polly
Amazon
TranscribeAmazon Lex
Amazon
Translate
Amazon
Comprehend
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data Visualization &
Analysis
Business Problem –
ML problem framing Data Collection
Data Integration
Data Preparation &
Cleaning
Feature Engineering
Model Training &
Parameter Tuning
Model Evaluation
Are Business
Goals met?
Model Deployment
Monitoring &
Debugging
– Predictions
YesNo
Data
Au
gm
en
tati
on
Featu
re
Au
gm
en
tati
on
Re-training
The Machine Learning process
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
End-to-End
Machine Learning
Platform
Zero setup Flexible Model
Training
Pay by the second
$
Build, train, and deploy machine learning models at scale
Amazon SageMaker
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Hands On!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Step 1: Create your notebook instance
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Step 2: Load, visualize and prepare Data
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Step 3: Train your model
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Step 4: Inference
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Next Steps
• Explore Amazon SageMaker built-in algorithms
• Build you ML endpoint into your web/mobile/IoT app
• Deploy your ML model to IoT devices using Amazon
Greengrass ML inference
• Collect new ground truth data from production
• Run A/B tests to find the best model
• Build automated data -> train -> deploy workflows
• Amazon CodePipeline, AWS Step Functions, etc.
• Use Amazon Mechanical Turk to help label your data
• Combine multiple ML models into an ML pipeline
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Build something cool on Amazon SageMaker
• Getting started with Amazon SageMaker:
https://aws.amazon.com/sagemaker/
• Use the Amazon SageMaker SDK:
• For Python: https://github.com/aws/sagemaker-python-sdk
• For Spark: https://github.com/aws/sagemaker-spark
• SageMaker Examples: https://github.com/awslabs/amazon-sagemaker-
examples
• Let us know what you build!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Please complete the session survey in
the summit mobile app.
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Thank You!