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Build Machine Learning Models with Amazon SageMaker (April 2019)

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Talk at AWS Summit Paris with Tarkett, 02/04/2019

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Build Machine Learning Models with Amazon SageMaker (April 2019)

  1. 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build Machine Learning Models with Amazon SageMaker Julien Simon Global Evangelist, AI & Machine Learning @julsimon
  2. 2. M L F R A M E W O R K S & I N F R A S T R U C T U R E The Amazon ML Stack: Broadest & Deepest Set of Capabilities A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D C O M P R E H E N D M E D I C A L L E XR E K O G N I T I O N V I D E O Vision Speech Chatbots A M A Z O N S A G E M A K E R B U I L D T R A I N F O R E C A S TT E X T R A C T P E R S O N A L I Z E D E P L O Y Pre-built algorithms & notebooks Data labeling (G R O U N D T R U T H ) One-click model training & tuning Optimization ( N E O ) One-click deployment & hosting M L S E R V I C E S F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e E C 2 P 3 & P 3 d n E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E Models without training data (REINFORCEMENT LEARNING) Algorithms & models ( A W S M A R K E T P L A C E ) Language Forecasting Recommendations NEW NEWNEW NEW NEW NEWNEW NEW NEW
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker: Build, Train, and Deploy ML Models at Scale Collect and prepare training data Choose and optimize your ML algorithm Train and Tune ML Models Set up and manage environments for training Deploy models in production Scale and manage the production environment 1 2 3
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Machine learning cycle Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Successful models require high-quality data
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Successful models require high-quality data
  7. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Ground Truth https://aws.amazon.com/blogs/aws/amazon-sagemaker-ground-truth-build-highly-accurate-datasets-and-reduce-labeling-costs-by-up-to-70 Easily integrate human labelers Get accurate results K E Y F E AT U R E S Automatic labeling via machine learning Ready-made and custom workflows for image bounding box, segmentation, and text Label management Quickly label training data Private and public human workforce
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Manage data on AWS Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build and train models using SageMaker Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Deploy models using SageMaker Business Problem ML problem framing Data collection Data integration Data preparation and cleaning Data visualization and analysis Feature engineering Model training and parameter tuning Model evaluation Monitoring and debugging Model deployment Predictions Are business goals met? YESNO Dataaugmentation Feature augmentation Re-training
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Collect and prepare training data Choose and optimize your ML algorithm Train and Tune ML Models Set up and manage environments for training Deploy models in production Scale and manage the production environment 1 2 3 Model compilation Elastic inference Inference pipelines P3DN, C5N TensorFlow on 256 GPUs Resume HPO tuning job New built-in algorithms scikit-learn environment Model marketplace Search Git integration Elastic inference
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS Machine Learning Marketplace
  13. 13. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T The Amazon SageMaker API • Python SDK orchestrating all Amazon SageMaker activity • High-level objects for algorithm selection, training, deploying, automatic model tuning, etc. • Spark SDK (Python & Scala) • AWS CLI: ‘aws sagemaker’ • AWS SDK: boto3, etc.
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Training code Factorization Machines Linear Learner Principal Component Analysis K-Means Clustering XGBoost And more Built-in Algorithms Bring Your Own ContainerBring Your Own Script Model options
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T IMAGE RECOGNITION | for the good of | PRODUCT SEARCH Sébastien BUTREAU sebastien.butreau@tarkett.com Group IT Projects & CCOE Manager Tarkett
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T → €2.8B Net sales (2018 figures) →13,000 employees → Present in more than 100 countries →1.3M square meters of flooring sold each day A worldwide leader in flooring & sports surfaces
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Could we recommend products to each user? VINYL & LINOLEUM CARPET WOOD & LAMINATE ACCESSORIES & RUBBER SPORTS SURFACES
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T «Building a bot»
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Why image search? Why does it make sense in the context of flooring? Field research showed that architects and designers were already leveraging image search engines Because getting results from an image search engine leads to inspiration
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Project Aladdin Deep Learning GPU instances
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Why we used Amazon SageMaker • Go quicker from idea to production • Distributed training out of the box • One line of code to deploy models • Almost the same cost as Amazon EC2 ($300/month)
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Demo: https://professionnels.tarkett.fr
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Next steps Explore new possibilities opened by image search • Find the best substitution when a product is out of stock • Understand « in-situ » the real user demand • Incorporate external factors (seasons, fashion, styles) • Position our products against the competition • Generate new designs
  25. 25. Merci! Sébastien BUTREAU sebastien.butreau@tarkett.com Group IT Projects & CCOE Manager Tarkett
  26. 26. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  27. 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Built-in algorithms orange:supervised,yellow:unsupervised Linear Learner: regression, classification Image Classification: Deep Learning (ResNet) Factorization Machines: regression, classification, recommendation Object Detection (SSD): Deep Learning (VGG or ResNet) K-Nearest Neighbors: non-parametric regression and classification Neural Topic Model: topic modeling XGBoost: regression, classification, ranking https://github.com/dmlc/xgboost Latent Dirichlet Allocation: topic modeling (mostly) K-Means: clustering Blazing Text: GPU-based Word2Vec, and text classification Principal Component Analysis: dimensionality reduction Sequence to Sequence: machine translation, speech to text and more Random Cut Forest: anomaly detection DeepAR: time-series forecasting (RNN) Object2Vec: general-purpose embedding IP Insights: usage patterns for IP addresses Semantic Segmentation: Deep Learning
  28. 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Demo: Text Classification with BlazingText https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia https://dl.acm.org/citation.cfm?id=3146354
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Demo: Image classification with Caltech-256 https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
  30. 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Collect and prepare training data Choose and optimize your ML algorithm Train and Tune ML Models Set up and manage environments for training Deploy models in production Scale and manage the production environment 1 2 3 Build Train Deploy
  31. 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Getting started http://aws.amazon.com/free https://ml.aws https://aws.amazon.com/sagemaker https://github.com/aws/sagemaker-python-sdk https://github.com/aws/sagemaker-spark https://github.com/awslabs/amazon-sagemaker-examples https://gitlab.com/juliensimon/ent321 https://medium.com/@julsimon https://gitlab.com/juliensimon/dlnotebooks
  32. 32. Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI and Machine Learning @julsimon https://medium.com/julsimon
  33. 33. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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