Build, Train, and Deploy ML Models at Scale
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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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Build, Train, and Deploy ML Models at Scale
- 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning @julsimon Build, train and deploy Machine Learning models at scale
- 2. Put Machine Learning in the hands of every developer and data scientist Our mission
- 3. Application Services Platform Services Frameworks & Infrastructure API-driven services: Vision, Language & Speech Services, Chatbots AWS ML Stack h t t p s : / / m l . a w s h t t p s : / / m e d i u m . c o m / @ j u l s i m o n / a - m a p - f o r - m a c h i n e - l e a r n i n g - o n - a w s - a 2 8 5 f c d 8 d 9 3 2 Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference. Develop sophisticated models with any framework, create managed, auto-scaling clusters of GPUs for large scale training, or run prediction on trained models.
- 4. Application Services Platform Services Frameworks & Infrastructure API-driven services: Vision, Language & Speech Services, Chatbots Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference. Develop sophisticated models with any framework, create managed, auto-scaling clusters of GPUs for large scale training, or run prediction on trained models. AWS ML Stack h t t p s : / / m l . a w s h t t p s : / / m e d i u m . c o m / @ j u l s i m o n / a - m a p - f o r - m a c h i n e - l e a r n i n g - o n - a w s - a 2 8 5 f c d 8 d 9 3 2
- 5. 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
- 6. Amazon SageMaker Pre-built notebooks for common problems K-Means Clustering Principal Component Analysis Neural Topic Modelling FactorizationMachines Linear Learner XGBoost Latent DirichletAllocation Image Classification Seq2Seq, And more! ALGORITHMS Apache MXNet, Chainer TensorFlow, PyTorch, scikit-learn FRAMEWORKS Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Built-in, high- performance algorithms Build
- 7. Amazon SageMaker Pre-built notebooks for common problems K-Means Clustering Principal Component Analysis Neural Topic Modelling FactorizationMachines Linear Learner XGBoost Latent DirichletAllocation Image Classification Seq2Seq, And more! ALGORITHMS Apache MXNet, Chainer TensorFlow, PyTorch, scikit-learn FRAMEWORKS Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Built-in, high- performance algorithms Build
- 8. Amazon SageMaker Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Train Deploy model in production Scale and manage the production environment Build
- 9. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization DeployTrainBuild
- 10. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Deploy Model compilation Elastic inference Inference pipelines TrainBuild 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. Working with Amazon SageMaker
- 13. 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.
- 14. 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
- 15. 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
- 17. 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
- 18. Demo: Image classification with Caltech-256 https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
- 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Blazing Text https://dl.acm.org/citation.cfm?id=3146354
- 20. Demo: Text Classification with BlazingText https://github.com/awslabs/amazon-sagemaker- examples/tree/master/introduction_to_amazon_algorithms/blazingtext_text_classification_dbpedia
- 21. 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
- 22. Demo: XGBoost AWS re:Invent 2018 workshop https://gitlab.com/juliensimon/ent321
- 24. 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
- 25. Demo: Sentiment analysis with Apache MXNet https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python- sdk/mxnet_sentiment_analysis_with_gluon.ipynb
- 26. Integration with AWS DeepLens
- 27. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
- 28. Use your own models with AWS DeepLens • AWS DeepLens can run TensorFlow, Caffe and Apache MXNet models • Inception • MobileNet • NasNet • ResNet • Etc. • Train or fine-tune your model on Amazon SageMaker • Deploy to AWS DeepLens with AWS Greengrass
- 29. Run inference and local actions on device Send insights to the Cloud Generic Deploy model and Lambda function Write inference code Setup Greengrass Architecture Train model
- 30. Demo: Image classification with Caltech-256 https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
- 31. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy
- 32. Selected Amazon SageMaker customers
- 33. © 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
- 34. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning @julsimon https://medium.com/@julsimon Thank you!