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Accelerate ML Workflows with Amazon SageMaker

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Julien SIMON
Julien SIMON

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Accelerate your Machine Learning workflows with Amazon SageMaker Julien Simon, AI Evangelist, EMEA @julsimon
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Platform Services AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms BuildPre-built notebook instances Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms One-click training for ML, DL, and custom algorithms BuildPre-built notebook instances Easier training with hyperparameter optimization Train Amazon SageMaker
© 2017, 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 BuildPre-built notebook instances Deploy Train Amazon SageMaker
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker customers https://aws.amazon.com/sagemaker/customers/
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demos 1.Off-the-shelf algorithm: classifying MNIST with XGBoost 2.Train with your own code: classifying the Iris data set with a DNN in Tensorflow 3.Deploying your own pre-trained model: clustering MNIST with k-means in scikit-learn 4.Bring your own algorithm: classifying the Iris data set with Decisions Trees in scikit-learn
FRAMEWORKS AND INTERFACES PLATFORM SERVICES APPLICATION SERVICES Amazon Rekognition Amazon Polly Amazon Lex Democratization of AI Amazon Rekognition Video Amazon Transcribe Amazon Comprehend Amazon SageMaker AWS DeepLens Amazon EMR Deep Learning AMI Amazon Translate
© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/machine-learning https://aws.amazon.com/blogs/ai https://medium.com/@julsimon
Thank you! Julien Simon, AI Evangelist, EMEA @julsimon

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Accelerate ML Workflows with Amazon SageMaker

  • 1. Accelerate your Machine Learning workflows with Amazon SageMaker Julien Simon, AI Evangelist, EMEA @julsimon
  • 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Platform Services AWS ML Stack Deploy machine learning models with high-performance machine learning algorithms, broad framework support, and one-click training, tuning, and inference.
  • 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. End-to-End Machine Learning Platform Zero setup Flexible Model Training Pay by the second $ Amazon SageMaker Build, train, and deploy machine learning models at scale
  • 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms BuildPre-built notebook instances Amazon SageMaker
  • 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Highly-optimized machine learning algorithms One-click training for ML, DL, and custom algorithms BuildPre-built notebook instances Easier training with hyperparameter optimization Train Amazon SageMaker
  • 6. © 2017, 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 BuildPre-built notebook instances Deploy Train Amazon SageMaker
  • 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker customers https://aws.amazon.com/sagemaker/customers/
  • 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demos 1.Off-the-shelf algorithm: classifying MNIST with XGBoost 2.Train with your own code: classifying the Iris data set with a DNN in Tensorflow 3.Deploying your own pre-trained model: clustering MNIST with k-means in scikit-learn 4.Bring your own algorithm: classifying the Iris data set with Decisions Trees in scikit-learn
  • 9. FRAMEWORKS AND INTERFACES PLATFORM SERVICES APPLICATION SERVICES Amazon Rekognition Amazon Polly Amazon Lex Democratization of AI Amazon Rekognition Video Amazon Transcribe Amazon Comprehend Amazon SageMaker AWS DeepLens Amazon EMR Deep Learning AMI Amazon Translate
  • 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/machine-learning https://aws.amazon.com/blogs/ai https://medium.com/@julsimon
  • 11. Thank you! Julien Simon, AI Evangelist, EMEA @julsimon