Advanced Machine Learning with Amazon SageMaker
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Talk @ O'Reilly AI, London, 10/10/2018
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Advanced Machine Learning with Amazon SageMaker
- 1. Advanced Machine Learning with Amazon SageMaker Julien Simon Principal Technical Evangelist, AI and Machine Learning @julsimon
- 2. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment for HTTPS or batch prediction Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy Easily build, train, and deploy ML models at any scale
- 3. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon ECR 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 Amazon SageMaker Training and predicting with Docker containers
- 4. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Training code Factorization Machines Linear Learner PrincipalComponent Analysis K-Means Clustering XGBoost And more Built-inAlgorithms BringYour Own ContainerBringYour Own Script Model options
- 6. Scalable algorithms implemented by Amazon orange: supervised, yellow: unsupervised Linear Learner: regression, classification Image Classification: Deep Learning (ResNet) Factorization Machines: regression, classification, recommendation Object Detection: 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 Dirichlet Allocation: topic modeling (mostly) K-Means: clustering BlazingText: GPU-based Word2Vec, and text classification Principal Component Analysis: reduction Sequence to Sequence: machine translation, speech to text and more Random Cut Forest: anomaly detection DeepAR: time-series forecasting (RNN)
- 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. DeepAR https://arxiv.org/abs/1704.04110
- 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Blazing Text https://dl.acm.org/citation.cfm?id=3146354
- 9. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker – latest algo features June 7 Hyper parameter optimization generally available July 12 Two new algos: k-Nearest-Neighbors, object detection (SSD) July 13 Improvements to DeepAR andWord2Vec. Linear Learner: multi-class classification. October 5 Image classification: multi-label classification, mixed-mode training.
- 10. Demo: Text Classification with BlazingText
- 11. Hyper Parameter Optimization Finding the optimal set of hyper parameters 1. Manual Search (”I know what I’m doing”) 2. Random Search (“Spray and pray”) 3. Grid Search (“X marks the spot”) • Typically training hundreds of models • Slow and expensive 4. HPO: use Machine Learning • Training fewer models • Gaussian Process Regression and Bayesian Optimization, https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html
- 12. Demo: Random Search vs HPO
- 13. Infrastructure
- 14. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon SageMaker – latest infra features July 17 Pipe mode supported forTensorflow training jobs (was already available for built-in algorithms) Batch transform for non-real time prediction August 14 SageMakerAPIs supported onAWS PrivateLink Sept. 4 New custom header for the InvokeEndPoint API
- 15. Batch transform • High-throughput method for generating inferences. • Ideal when: • You want to predict an entire dataset and store results online. • You don't need a persistent endpoint for applications. • You don't need low latency predictions. • You want to preprocess your data. • For large datasets or data of indeterminate size, you can create an infinite stream. • Train as usual, create aTransformer object and predict. • Results are available in JSON format in Amazon S3.
- 17. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Pipe mode: streaming data to training instances Data Size Memory Data Size Time/Cost
- 18. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Factorization Machines Log_loss F1 Score Seconds SageMaker 0.494 0.277 820 Other (10 iter) 0.516 0.190 650 Other (20 iter) 0.507 0.254 1300 Other (50 iter) 0.481 0.313 3250 Advertising data set, click prediction, 1TB. m4.4xlarge machines, perfect scaling. $- $20.00 $40.00 $60.00 $80.00 $100.00 $120.00 $140.00 $160.00 $180.00 $200.00 1 2 3 4 5 6 7 8CostinDollars Billable Time in Hours 10 machines 20 machines 30 machines 4050
- 20. AWS PrivateLink: private access to endpoints SageMaker Endpoint
- 21. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment for HTTPS or batch prediction Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy Easily build, train, and deploy ML models at any scale
- 22. Thank you! Julien Simon PrincipalTechnical Evangelist, AI and Machine Learning @julsimon https://ml.aws https://aws.amazon.com/blogs/ai https://aws.amazon.com/sagemaker https://github.com/awslabs/amazon-sagemaker-examples https://github.com/aws/sagemaker-python-sdk https://github.com/aws/sagemaker-spark https://medium.com/@julsimon https://youtube.com/juliensimonfr/