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Machine Learning: From
Notebook to Production
with Amazon Sagemaker
Julien Simon, AI Evangelist, EMEA
@julsimon
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Platform
Services
AWS ML Stack
Deploy machine learning models with high-performance machine learning
algorithms, broad framework support, and one-click training, tuning, and
inference.
© 2017, 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
YesNo
DataAugmentation
Feature
Augmentation
The Machine Learning Process
Re-training
Predictions
© 2017, 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
YesNo
DataAugmentation
Feature
Augmentation
Problem discovery
Re-training
• Help formulate the right
questions
• Domain Knowledge
Predictions
© 2017, 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
YesNo
DataAugmentation
Feature
Augmentation
Retraining
• Need a data platform?
• Amazon S3
• AWS Glue
• Amazon Athena
• Amazon EMR
• Amazon Redshift
Spectrum
Integration
Predictions
© 2017, 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
YesNo
DataAugmentation
Feature
Augmentation
Retraining
Model Training
Predictions
• Setup and manage Notebook
Environments
• Setup and manage Training
Clusters
• Write Data Connectors
• Scale ML algorithms to large
datasets
• Distribute ML training
algorithm to multiple
machines
• Secure Model artifacts
© 2017, 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
YesNo
DataAugmentation
Feature
Augmentation
Retraining
Model Deployment
Predictions
• Setup and manage Model
Inference Clusters
• Manage and Scale Model
Inference APIs
• Monitor and Debug Model
Predictions
• Models versioning and
performance tracking
• Automate New Model
version promotion to
production (A/B testing)
© 2017, 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
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Highly-optimized
machine learning
algorithms
BuildPre-built notebook
instances
Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Highly-optimized
machine learning
algorithms
One-click training for
ML, DL, and custom
algorithms
BuildPre-built notebook
instances
Easier training with
hyperparameter
optimization
Train
Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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
Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon ECR
Model Training (on EC2)
Model Hosting (on EC2)
Trainingdata
Modelartifacts
Training code Helper code
Helper codeInference code
GroundTruth
Client application
Inference code
Training code
Inference requestInference response
Inference Endpoint
Amazon SageMaker
Digital Globe
http://blog.digitalglobe.com/industry/using-machine-learning-to-save-
money-on-cloud-data-storage/
https://www.youtube.com/watch?v=mkKkSRIxU8M
In the last 18 years DigitalGlobe has
been operating Earth imaging satellites,
they have collected over 100 PB of
imagery.
There is a trade-off between how
quickly data can be accessed and how
much it will cost to store.
Working with the ML Lab, Digital Globe
built a predictive model that will
reduce cloud storage costs for their
imagery archive by 50%.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Demos
1. Use a built-in algorithm:
fine-tuning a pre-trained image classification model
2. Bring your own training code:
distributed training from scratch with MXNet and Gluon
3. Bring your own pre-trained model:
classifying the Iris data set with a pre-trained TensorFlow model
4. Bring your own container:
classifying the Iris data set with Decisions Trees in scikit-learn
© 2017, 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
$
Amazon SageMaker
Build, train, and deploy machine learning models at scale
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Resources
https://aws.amazon.com/machine-learning
https://aws.amazon.com/blogs/ai
https://aws.amazon.com/sagemaker
An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4
https://medium.com/@julsimon
Thank you!
Julien Simon, AI Evangelist, EMEA
@julsimon

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From notebook to production with Amazon Sagemaker

  • 1. Machine Learning: From Notebook to Production with Amazon Sagemaker Julien Simon, AI Evangelist, EMEA @julsimon
  • 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Platform Services AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
  • 3. © 2017, 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 YesNo DataAugmentation Feature Augmentation The Machine Learning Process Re-training Predictions
  • 4. © 2017, 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 YesNo DataAugmentation Feature Augmentation Problem discovery Re-training • Help formulate the right questions • Domain Knowledge Predictions
  • 5. © 2017, 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 YesNo DataAugmentation Feature Augmentation Retraining • Need a data platform? • Amazon S3 • AWS Glue • Amazon Athena • Amazon EMR • Amazon Redshift Spectrum Integration Predictions
  • 6. © 2017, 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 YesNo DataAugmentation Feature Augmentation Retraining Model Training Predictions • Setup and manage Notebook Environments • Setup and manage Training Clusters • Write Data Connectors • Scale ML algorithms to large datasets • Distribute ML training algorithm to multiple machines • Secure Model artifacts
  • 7. © 2017, 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 YesNo DataAugmentation Feature Augmentation Retraining Model Deployment Predictions • Setup and manage Model Inference Clusters • Manage and Scale Model Inference APIs • Monitor and Debug Model Predictions • Models versioning and performance tracking • Automate New Model version promotion to production (A/B testing)
  • 8. © 2017, 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 $ Amazon SageMaker Build, train, and deploy machine learning models at scale
  • 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms BuildPre-built notebook instances Amazon SageMaker
  • 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms One-click training for ML, DL, and custom algorithms BuildPre-built notebook instances Easier training with hyperparameter optimization Train Amazon SageMaker
  • 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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 Amazon SageMaker
  • 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon ECR Model Training (on EC2) Model Hosting (on EC2) Trainingdata Modelartifacts Training code Helper code Helper codeInference code GroundTruth Client application Inference code Training code Inference requestInference response Inference Endpoint Amazon SageMaker
  • 13. Digital Globe http://blog.digitalglobe.com/industry/using-machine-learning-to-save- money-on-cloud-data-storage/ https://www.youtube.com/watch?v=mkKkSRIxU8M In the last 18 years DigitalGlobe has been operating Earth imaging satellites, they have collected over 100 PB of imagery. There is a trade-off between how quickly data can be accessed and how much it will cost to store. Working with the ML Lab, Digital Globe built a predictive model that will reduce cloud storage costs for their imagery archive by 50%.
  • 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demos 1. Use a built-in algorithm: fine-tuning a pre-trained image classification model 2. Bring your own training code: distributed training from scratch with MXNet and Gluon 3. Bring your own pre-trained model: classifying the Iris data set with a pre-trained TensorFlow model 4. Bring your own container: classifying the Iris data set with Decisions Trees in scikit-learn
  • 15. © 2017, 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 $ Amazon SageMaker Build, train, and deploy machine learning models at scale
  • 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/machine-learning https://aws.amazon.com/blogs/ai https://aws.amazon.com/sagemaker An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4 https://medium.com/@julsimon
  • 17. Thank you! Julien Simon, AI Evangelist, EMEA @julsimon