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Speed up your Machine Learning workflows with build-in algorithms
Amazon SageMaker provides several machine learning algorithms and tools to help speed up workflows: (1) It offers many pre-built machine learning algorithms like linear learner, factorization machines, and DeepAR for time series forecasting. (2) These algorithms are highly optimized and can train models up to 10 times faster than alternatives. (3) Amazon SageMaker also allows users to integrate their own algorithms or use open source tools like Apache Spark and MXNet.
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Speed up your Machine Learning workflows with build-in algorithms
- 1. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Julien Simon Principal AI/ML Evangelist, Amazon Web Services Speed up your Machine Learning workflows with built-in algorithms @julsimon
- 2. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. One-click training for ML, DL, and custom algorithms Easier training with hyperparameter optimization Highly-optimized machine learning algorithms Deployment without engineering effort Fully-managed hosting at scale Build Pre-built notebook instances Deploy Train Amazon SageMaker
- 3. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Training code • Matrix Factorization • Regression • Principal Component Analysis • K-Means Clustering • Gradient Boosted Trees • And More! Amazon provided Algorithms Bring Your Own Container Amazon SageMaker: model options Bring Your Own Script IM Estimators in Apache Spark
- 4. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Streaming datasets, for cheaper training Train faster, in a single pass Greater reliability on extremely large datasets Choice of several ML algorithms Amazon SageMaker: 10x better algorithms
- 5. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Infinitely scalable algorithms
- 6. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Streaming GPU State
- 7. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Streaming Data Size Memory Data Size Time/Cost
- 8. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Distributed GPU State GPU State GPU State
- 9. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Shared State GPU GPU GPU Local State Shared State Local State Local State
- 10. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Cost vs. Time $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Best Alternative Amazon SageMaker
- 11. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Linear Learner Regression (mean squared error) SageMaker Other 1.02 1.06 1.09 1.02 0.332 0.183 0.086 0.129 83.3 84.5 Classification (F1 Score) SageMaker Other 0.980 0.981 0.870 0.930 0.997 0.997 0.978 0.964 0.914 0.859 0.470 0.472 0.903 0.908 0.508 0.508 30 GB datasets for web-spam and web-url classification 0 0.2 0.4 0.6 0.8 1 1.2 0 5 10 15 20 25 30 CostinDollars Billable time in Minutes sagemaker-url sagemaker-spam other-url other-spam
- 12. © 2018, AmazonWeb 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 Click Prediction 1 TB advertising dataset, 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
- 13. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Demo: building a movie recommender with Factorization Machines h t t p s : / / m e d i u m . c o m / @ j u l s i m o n / b u i l d i n g - a - m o v i e - r e c o m m e n d e r - w i t h - f a c t o r i z a t i o n - m a c h i n e s - o n - a m a z o n - s a g e m a k e r - c e d b f c 8 c 9 3 d 8
- 14. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. 0 1 2 3 4 5 6 7 8 10 100 500 BillableTimeinMinutes Number of Clusters sagemaker other K-Means Clustering k SageMaker Other Text 1.2GB 10 1.18E3 1.18E3 100 1.00E3 9.77E2 500 9.18.E2 9.03E2 Images 9GB 10 3.29E2 3.28E2 100 2.72E2 2.71E2 500 2.17E2 Failed Videos 27GB 10 2.19E2 2.18E2 100 2.03E2 2.02E2 500 1.86E2 1.85E2 Advertising 127GB 10 1.72E7 Failed 100 1.30E7 Failed 500 1.03E7 Failed Synthetic 1100GB 10 3.81E7 Failed 100 3.51E7 Failed 500 2.81E7 Failed Running Time vs. Number of Clusters ~10x Faster!
- 15. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Principal Component Analysis (PCA) More than 10x faster at a fraction the cost! 0.00 20.00 40.00 60.00 80.00 100.00 120.00 8 10 20 Mb/Sec/Machine Number of Machines other sagemaker-deterministic sagemaker-randomized Cost vs. Time Throughput and Scalability 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 10 20 30 40 50 CostinDollars Billable time in Minutes other sagemaker-deterministic sagemaker-randomized
- 16. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Neural Topic Modeling Perplexity vs. Number of Topic Encoder: feedforward net Input term counts vector Document Posterior Sampled Document Representation Decoder: Softmax Output term counts vector 0 2000 4000 6000 8000 10000 12000 0 50 100 150 200 Perplexity Number of Topics NTM Other
- 17. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. DeepAR: Time Series Forecasting Mean absolute percentage error P90 Loss DeepAR R DeepAR R traffic Hourly occupancy rate of 963 Bay Area freeways 0.14 0.27 0.13 0.24 electricity Electricity use of 370 homes over time 0.07 0.11 0.08 0.09 pageviews Page view hits of websites 10k 0.32 0.32 0.44 0.31 180k 0.32 0.34 0.29 NA One hour on p2.xlarge, $1 Input Network
- 18. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. DeepAR https://arxiv.org/abs/1704.04110
- 19. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Demo: predicting world temperature with DeepAR h t t p s : / / m e d i u m . c o m / @ j u l s i m o n / p r e d i c t i n g - w o r l d - t e m p e r a t u r e - w i t h - t i m e - s e r i e s - a n d - d e e p a r - o n - a m a z o n - s a g e m a k e r - e 3 7 1 c f 9 4 d d b 5
- 20. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. More built-in algorithms
- 21. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Spectral LDA Training Time vs. Number of Topics 0 50 100 150 200 250 0 20 40 60 80 100TrainingTimeinMinutes Number of Topics lda-data-a lda-data-b other-data-a other-data-b
- 22. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Boosted Decision Trees Throughput vs. Number of Machines XGBoost is one of the most commonly used classifiers. 0 200 400 600 800 1000 1200 1400 0 10 20 30 40 50 60 70 ThroughputinMB/Sec Number of Machines (C4.8xLarge) https://github.com/dmlc/xgboost
- 23. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Sequence to Sequence English-German Translation 0 5 10 15 20 25 0 5 10 15 20 25 BLEUScore Billable Time in Hours P2.16x P2.8x P2.x Best known result! • Based on Sockeye and Apache MXNet. • Multi-GPU. • Can be used for Neural Machine Translation. • Supports both RNN/CNN as encoder/decoder
- 24. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. https://arxiv.org/abs/1712.05690 https://github.com/awslabs/sockeye Sockeye
- 25. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Image Classification • ResNet implementation with Apache MXNet. • More networks to come. • Transfer learning: begin with a model already trained on ImageNet! 0 0.5 1 1.5 2 2.5 3 3.5 0 1 2 3 4 5 Speedup Number of Machines (P2) Linear Speedup with Horizontal Scaling
- 26. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Demo: fine-tuning an image classification model h t t p s : / / m e d i u m . c o m / @ j u l s i m o n / i m a g e - c l a s s i f i c a t i o n - o n - a m a z o n - s a g e m a k e r - 9 b 6 6 1 9 3 c 8 b 5 4
- 27. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Latest addition: Blazing Text https://dl.acm.org/citation.cfm?id=3146354
- 28. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Resources https://aws.amazon.com/machine-learning https://aws.amazon.com/blogs/ai https://aws.amazon.com/sagemaker (free tier available) https://github.com/awslabs/amazon-sagemaker-examples An overview of Amazon SageMaker https://www.youtube.com/watch?v=ym7NEYEx9x4 https://medium.com/@julsimon
- 29. © 2018, AmazonWeb Services, Inc. or Its Affiliates. All rights reserved. Thank you! Julien Simon Principal AI/ML Evangelist, Amazon Web Services @julsimon