rights reserved. Build, train, and deploy Machine Learning models at scale A M L 3 D A C H 2 0 1 9 2 0 1 9 Antje Barth Technical Evangelist AI and Machine Learning Amazon Web Services @anbarth
rights reserved. FRAMEWORKS INTERFACES INFRASTRUCTURE AI Services The AWS ML stack VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS ML Services ML Frameworks + Infrastructure A M A Z O N P O L L Y A M A Z O N T R A N S C R I B E A M A Z O N T R A N S L A T E A M A Z O N C O M P R E H E N D & A M A Z O N C O M P R E H E N D M E D I C A L A M A Z O N L E X A M A Z O N F O R E C A S T A M A Z O N R E K O G N I T I O N I M A G E A M A Z O N R E K O G N I T I O N V I D E O A M A Z O N T E X T R A C T A M A Z O N P E R S O N A L I Z E F P G A s E C 2 P 3 & P 3 D N E C 2 G 4 E C 2 C 5 A W S I N F E R E N T I A A W S I o T G R E E N G R A S S A M A Z O N E L A S T I C I N F E R E N C E A W S D L C O N T A I N E R S & A M I s A M A Z O N E L A S T I C K U B E R N E T E S S E R V I C E A M A Z O N E L A S T I C C O N T A I N E R S E R V I C E Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting h t t p s : / / m l . a w s
rights reserved. FRAMEWORKS INTERFACES INFRASTRUCTURE AI Services VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS ML Services ML Frameworks + Infrastructure A M A Z O N P O L L Y A M A Z O N T R A N S C R I B E A M A Z O N T R A N S L A T E A M A Z O N C O M P R E H E N D & A M A Z O N C O M P R E H E N D M E D I C A L A M A Z O N L E X A M A Z O N F O R E C A S T A M A Z O N R E K O G N I T I O N I M A G E A M A Z O N R E K O G N I T I O N V I D E O A M A Z O N T E X T R A C T A M A Z O N P E R S O N A L I Z E F P G A s E C 2 P 3 & P 3 D N E C 2 G 4 E C 2 C 5 A W S I N F E R E N T I A A W S I o T G R E E N G R A S S A M A Z O N E L A S T I C I N F E R E N C E A W S D L C O N T A I N E R S & A M I s A M A Z O N E L A S T I C K U B E R N E T E S S E R V I C E A M A Z O N E L A S T I C C O N T A I N E R S E R V I C E Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting The AWS ML stack h t t p s : / / m l . a w s
rights reserved. Amazon Machine Images pre-configured with deep learning frameworks and GPU/CPU drivers AWS Deep Learning AMIs AWS Deep Learning Containers Docker images pre-installed with deep learning frameworks https://aws.amazon.com/ machine- learning/containers http://aws.amazon.com/ machine-learning/amis AWS helps you build self-managed Machine Learning environments with DYI services
rights reserved. FRAMEWORKS INTERFACES INFRASTRUCTURE AI Services VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS ML Services ML Frameworks + Infrastructure A M A Z O N P O L L Y A M A Z O N T R A N S C R I B E A M A Z O N T R A N S L A T E A M A Z O N C O M P R E H E N D & A M A Z O N C O M P R E H E N D M E D I C A L A M A Z O N L E X A M A Z O N F O R E C A S T A M A Z O N R E K O G N I T I O N I M A G E A M A Z O N R E K O G N I T I O N V I D E O A M A Z O N T E X T R A C T A M A Z O N P E R S O N A L I Z E F P G A s E C 2 P 3 & P 3 D N E C 2 G 4 E C 2 C 5 A W S I N F E R E N T I A A W S I o T G R E E N G R A S S A M A Z O N E L A S T I C I N F E R E N C E A W S D L C O N T A I N E R S & A M I s A M A Z O N E L A S T I C K U B E R N E T E S S E R V I C E A M A Z O N E L A S T I C C O N T A I N E R S E R V I C E Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting The AWS ML stack h t t p s : / / m l . a w s
rights reserved. 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? YES NO Data augmentation Feature augmentation Re-training
rights reserved. Amazon SageMaker Ground Truth Build scalable and cost-effective labeling workflows K E Y F E A T U R E S Automatic labeling via machine learning Ready-made and custom workflows for image bounding box, segmentation, and text Integrated with Deep Learning algorithms in Amazon SageMaker Private and public human workforce
rights reserved. Prepare your dataset for Machine Learning 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? YES NO Data augmentation Feature augmentation Re-training
rights reserved. Build, train and deploy models using SageMaker 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? YES NO Data augmentation Feature augmentation Re-training
rights reserved. Notebook instances • Fully managed EC2 instances, from T2 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
rights reserved. The Amazon SageMaker API • 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.
rights reserved. Training data Model Hosting Helper code Inference code Ground Truth Client application Inference code Training code Inference request Inference response Inference endpoint Amazon S3 Amazon EFS Amazon FSx for Lustre Model artifacts Amazon S3 NEW! Training code Helper code Model Training (on demand or spot) NEW!
rights reserved. 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 Bring Your 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 NEW! Model options
rights reserved. 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!
rights reserved. Training ResNet-50 with the ImageNet dataset using our optimized build of TensorFlow 1.11 on a c5.18xlarge instance type is designed to be 11x faster than training on the stock binaries TensorFlow on AWS C5 instances (Intel Skylake) 65% 90% P3 instances (NVIDIA V100)
rights reserved. Getting started https://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
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