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Build, train and deploy ML models with SageMaker (October 2019)

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Talk at AWS Summit Switzerland (02/10/2019)

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Build, train and deploy ML models with SageMaker (October 2019)

  1. 1. S U M M I T Switzerlan d
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build,Train and Deploy Machine Learning Models onAmazonSageMaker Julien Simon Global Evangelist, AI & Machine Learning AmazonWeb Services @julsimon Stéphane Cheikh Director, Portfolio Evolution using Artificial Intelligence SITA
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Machinelearning 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
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AmazonSageMaker 1 2 3 Same service and APIs from experimentation to production
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build your dataset 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. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Prepare your datasetfor MachineLearning 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. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build,trainand 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
  8. 8. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Notebook instances • Fully managed EC2 instances, fromT2 to P3 • G4 and R5 now available for inference – NEW! • Pre-installed with Jupyter and Conda environments • Python 2.7 & 3.6 • Open-source libraries (TensorFlow,Apache MXNet, etc.) • Beta support for R – NEW! • Amazon Elastic Inference for cost-effective GPU acceleration • Lifecycle configurations • VPC, encryption, etc. • Get to work in minutes, zero setup
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T TheAmazonSageMakerAPI • Python SDK orchestrating all Amazon SageMaker activity • High-level objects for algorithm selection, training, deploying, automatic model tuning, etc. https://github.com/aws/sagemaker-python-sdk • Spark SDK (Python & Scala) https://github.com/aws/sagemaker-spark/tree/master/sagemaker-spark-sdk • AWS SDK • Service-level APIs for scripting and automation • CLI: ‘aws sagemaker’ • Language SDKs: boto3, etc.
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Trainingdata Model Hosting Helper codeInference code GroundTruth Client application Inference code Training code Inference requestInference response Inference endpoint Amazon S3 Amazon EFS Amazon FSx for Lustre Modelartifacts Amazon S3 NEW! Training code Helper code Model Training (on demand or spot) NEW!
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Model options Training code Factorization Machines Linear Learner Principal Component Analysis K-Means Clustering XGBoost And more Built-in Algorithms (17) No ML coding required No infrastructure work required Distributed training Pipe mode BringYour Own Container Full control, run anything! R, C++, etc. No infrastructure work required Built-in Frameworks Bring your own code: Script mode Open-source containers No infrastructure work required Distributed training Pipe mode NEW!
  13. 13. © 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 and classification NeuralTopic Model:Topic modeling XGBoost: Regression, classification, ranking https://github.com/dmlc/xgboost Latent Dirichlet Allocation:Topic modeling (mostly) K-Means: Clustering BlazingText: 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
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Built-in frameworks: just add your code • Built-in containers for training and prediction • Open-source, e.g., https://github.com/aws/sagemaker-tensorflow-containers • Build them, run them on your own machine, customize them, etc. • Local mode: train and predict on your notebook instance, or on your local machine • Script mode: migrate existing code to SageMaker with minimal changes NEW !
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Training ResNet-50 with the ImageNet dataset using our optimized build ofTensorFlow 1.11 on a c5.18xlarge instance type is designed to be 11x faster than training on the stock binaries TensorFlow onAWS C5 instances (Intel Skylake) 65% 90% P3 instances (NVIDIAV100)
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Apache MXNet: Deep learning for enterprise developers • Gluon CV, Gluon NLP, Gluon TS ONNX 2x faster Java Scala
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Demo: Image classification with Keras/TensorFlow + Script Mode + Managed SpotTraining + Elastic Inference https://aws.amazon.com/blogs/machine-learning/train-and-deploy-keras-models-with-tensorflow-and-apache-mxnet-on- amazon-sagemaker/ https://gitlab.com/juliensimon/dlnotebooks/tree/master/keras/05-keras-blog-post
  18. 18. © 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/dlnotebooks
  19. 19. Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI & Machine Learning AmazonWeb Services @julsimon Stéphane Cheikh Director, Portfolio Evolution using Artificial Intelligence SITA
  20. 20. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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