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Build, Train and Deploy Machine Learning Models at Scale (April 2019)
Amazon SageMaker is a fully managed service that allows users to build, train and deploy machine learning models at scale. It provides pre-built algorithms, tools for data labeling, one-click training and tuning of models, and deployment of trained models. SageMaker handles the details of setting up, managing and scaling machine learning infrastructure on AWS.
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Build, Train and Deploy Machine Learning Models at Scale (April 2019)
- 1. © 2019, AmazonWeb 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 Learningin the hands of every developer and data scientist Our mission
- 3. M L FR 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, andDeploy 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 MLproblem 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 onAWS 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 trainmodels 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 usingSageMaker 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 hostingwith 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
- 10.
- 11. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 12. TheAmazon SageMakerAPI • PythonSDK 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 (onEC2) 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 LinearLearner PrincipalComponent Analysis K-Means Clustering XGBoost And more Built-inAlgorithms BringYour Own ContainerBringYour Own Script Model options
- 15. © 2019, AmazonWeb 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 NeuralTopic Model: topic modeling XGBoost: regression, classification, ranking https://github.com/dmlc/xgboost Latent DirichletAllocation: topic modeling (mostly) K-Means: clustering BlazingText:GPU-basedWord2Vec, and text classification Principal ComponentAnalysis: dimensionality reduction Sequence to Sequence: machine translation, speech speech to text and more RandomCut 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 withCaltech-256 https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
- 18. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. Blazing Text https://dl.acm.org/citation.cfm?id=3146354
- 19. Demo: Text Classification withBlazingText https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia
- 20. XGBoost • Open Sourceproject • 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:Invent2018 workshop https://gitlab.com/juliensimon/ent321
- 22. © 2019, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 23. Demo: Keras/TensorFlow CNN onCIFAR-10 https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/tensorflow_keras_cifar10/tensorflow_keras_CIFAR10.ipynb
- 24. Demo: Sentiment analysis withApache MXNet https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/mxnet_sentiment_analysis_with_gluon.ipynb
- 25. Amazon SageMaker Fully managed hostingwith 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, AmazonWeb 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
Editor's Notes
- #2 This talk is for builders I want to build a new app or website and I want it to work on every platform I want easily leverage AWS from my existing web or mobile apps and I don’t want to rewrite everything I want to learn about cool new development tools like React, GraphQL, CLIs, and serverless technologies I want to focus less on ops and configuration and more on my product If you recognize yourself in one of the previous sentences, you’re in the right room. TODO for the presenter : adjust the banner to the conference you will speak to.
- #10 Once your model is trained and tuned, SageMaker makes it easy to deploy in production so you can start generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. It also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results. For maximum versatility, we designed Amazon SageMaker in three modules – Build, Train, and Deploy – that can be used together or independently as part of any existing ML workflow you might already have in place.
- #17 Seq2Seq: used by Amazon Translate and AWS Sockeye LDA: used by Amazon Comprehend
- #21 Source: XGBoost research paper https://arxiv.org/abs/1603.02754
- #26 Once your model is trained and tuned, SageMaker makes it easy to deploy in production so you can start generating predictions on new data (a process called inference). Amazon SageMaker deploys your model on an auto-scaling cluster of Amazon EC2 instances that are spread across multiple availability zones to deliver both high performance and high availability. It also includes built-in A/B testing capabilities to help you test your model and experiment with different versions to achieve the best results. For maximum versatility, we designed Amazon SageMaker in three modules – Build, Train, and Deploy – that can be used together or independently as part of any existing ML workflow you might already have in place.
- #28 TODO for the presenter : adjust the banner to the conference you will speak to.