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Build, train, and deploy ML models at scale.pdf

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Amazon Web Services
Amazon Web Services

Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build, train and deploy Machine Learning models at scale Julien Simon Global Evangelist, AI & Machine Learning @julsimon
Put Machine Learning in the hands of every developer and data scientist Our mission
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
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
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
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
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
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
Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms and frameworks One-click training Hyperparameter optimization DeployTrainBuild 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
Machine Learning Marketplace
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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.
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
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
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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
Demo: Image classification with Caltech-256 https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Blazing Text https://dl.acm.org/citation.cfm?id=3146354
Demo: Text Classification with BlazingText https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia
XGBoost • Open Source project • Popular tree-based algorithm for regression, classification and ranking • Builds a collection of trees. • Handles missing values and sparse data • Supports distributed training • Can work with data sets larger than RAM https://github.com/dmlc/xgboost https://xgboost.readthedocs.io/en/latest/ https://arxiv.org/abs/1603.02754
Demo: XGBoost AWS re:Invent 2018 workshop https://gitlab.com/juliensimon/ent321
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Demo: Keras/TensorFlow CNN on CIFAR-10 https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/tensorflow_keras_cifar10/tensorflow_keras_CIFAR10.ipynb
Demo: Sentiment analysis with Apache MXNet https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/mxnet_sentiment_analysis_with_gluon.ipynb
Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms and frameworks One-click training Hyperparameter optimization Build Train Deploy
© 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
Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning @julsimon https://medium.com/@julsimon

More Related Content

Build, train, and deploy ML models at scale.pdf

  • 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build, train and deploy Machine Learning models at scale Julien Simon Global Evangelist, AI & Machine Learning @julsimon
  • 2. Put Machine Learning in the hands of every developer and data scientist Our mission
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms and frameworks One-click training Hyperparameter optimization DeployTrainBuild 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
  • 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 12. 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.
  • 13. 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
  • 14. 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
  • 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 16. 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
  • 17. Demo: Image classification with Caltech-256 https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
  • 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Blazing Text https://dl.acm.org/citation.cfm?id=3146354
  • 19. Demo: Text Classification with BlazingText https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia
  • 20. XGBoost • Open Source project • Popular tree-based algorithm for regression, classification and ranking • Builds a collection of trees. • Handles missing values and sparse data • Supports distributed training • Can work with data sets larger than RAM https://github.com/dmlc/xgboost https://xgboost.readthedocs.io/en/latest/ https://arxiv.org/abs/1603.02754
  • 21. Demo: XGBoost AWS re:Invent 2018 workshop https://gitlab.com/juliensimon/ent321
  • 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  • 23. Demo: Keras/TensorFlow CNN on CIFAR-10 https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/tensorflow_keras_cifar10/tensorflow_keras_CIFAR10.ipynb
  • 24. Demo: Sentiment analysis with Apache MXNet https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/mxnet_sentiment_analysis_with_gluon.ipynb
  • 25. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms and frameworks One-click training Hyperparameter optimization Build Train Deploy
  • 26. © 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
  • 27. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning @julsimon https://medium.com/@julsimon