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Sviluppa, addestra e distribuisci modelli di machine learning.pdf

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2° episodio - Speaker: Giuseppe Porcelli, Solutions Architect AWS

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Sviluppa, addestra e distribuisci modelli di machine learning.pdf

  1. 1. © 2019, Amazon Web Services, Inc. or its Affiliates. Giuseppe Angelo Porcelli Principal Solutions Architect Amazon Web Services EMEA Sviluppa, addestra e distribuisci modelli di Machine Learning Serie webinar in Italiano
  2. 2. © 2019, Amazon Web Services, Inc. or its Affiliates. Artificial Intelligence. Get Started. Serie webinar in Italiano Today 27 Giugno25 Giugno 11 Giugno
  3. 3. © 2019, Amazon Web Services, Inc. or its Affiliates. THE AWS MACHINE LEARNING STACK M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S M L P L A T F O R M S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N V I D E O Vision Speech Language Chatbots F O R E C A S T Forecasting T E X T R A C T P E R S O N A L I Z E Recommendations
  4. 4. © 2019, Amazon Web Services, Inc. or its Affiliates. THE AWS MACHINE LEARNING STACK M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N V I D E O Vision Speech Language Chatbots F O R E C A S T Forecasting T E X T R A C T P E R S O N A L I Z E Recommendations M L P L A T F O R M S A M A Z O N S A G E M A K E R B U I L D T R A I N D E P L O Y Pre-built algorithms & notebooks Data labeling (G R O U N D T R U T H ) One-click model training & tuning Optimization (N E O ) One-click deployment & hosting Reinforcement learningAlgorithms & models ( A W S M A R K E T P L A C E F O R M A C H I N E L E A R N I N G )
  5. 5. © 2019, Amazon Web Services, Inc. or its Affiliates. THE AWS MACHINE LEARNING STACK M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N V I D E O Vision Speech Language Chatbots F O R E C A S T Forecasting T E X T R A C T P E R S O N A L I Z E Recommendations M L P L A T F O R M S F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e E C 2 P 3 & P 3 D N E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E A M A Z O N S A G E M A K E R B U I L D T R A I N D E P L O Y Pre-built algorithms & notebooks Data labeling (G R O U N D T R U T H ) One-click model training & tuning Optimization (N E O ) One-click deployment & hosting Reinforcement learningAlgorithms & models ( A W S M A R K E T P L A C E F O R M A C H I N E L E A R N I N G ) A W S D E E P L E A R N I N G A M I
  6. 6. © 2019, Amazon Web Services, Inc. or its Affiliates. MORE MACHINE LEARNING HAPPENS ON AWS THAN ANYWHERE ELSE 10,000+ customers | 2x the customer references | 85% of TensorFlow projects in the cloud happen on AWS
  7. 7. © 2019, Amazon Web Services, Inc. or its Affiliates. THE MACHINE LEARNING PROCESS 1 2 3 1 2 3
  8. 8. © 2019, Amazon Web Services, Inc. or its Affiliates. Amazon SageMaker A managed service that provides the quickest and easiest way for data scientists and developers to get ML models from idea to production
  9. 9. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER: BUILD, TRAIN AND DEPLOY ML MODELS 1 2 3
  10. 10. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER GROUND TRUTH Easily integrate human labelers Get accurate results 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 Label management Quickly label training data Private and public human workforce Label machine learning training data easily and accurately
  11. 11. © 2019, Amazon Web Services, Inc. or its Affiliates. Amazon SageMaker AMAZON SAGEMAKER NOTEBOOK INSTANCES • Fully-managed Jupyter Notebook with flexible choice of ML compute instances • Anaconda packages and libraries for common Deep Learning platforms • Jupyter/JupyterLab interfaces • Over 200 example notebooks • Git integration • Lifecycle Configurations • Access to Terminal through Jupyter • Root/non-root access • VPC & encryption VPC ML Compute Instance CPU GPU ML Storage CreateNotebookInstance() HTTPS Endpoint Client Browser
  12. 12. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER: BUILD, TRAIN AND DEPLOY ML MODELS
  13. 13. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER BUILT-IN ALGORITHMS Algorithms for “infinite scale” Distributed by default Train on a data stream Checkpoint for re-training Single pass training Not memory bound K-Means K-Nearest Neighbors (k-NN) PCA Latent Dirichlec Allocation (LDA) Factorization Machines Linear Learner Neural Topic Model (NTM) Random Cut Forest (RCF) Sequence to Sequence (seq2seq) XGBoost Image Classification Object Detection DeepAR Forecasting BlazingText IP Insights Semantic Segmentation Object2Vec UnsupervisedSupervised
  14. 14. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER: BUILD, TRAIN AND DEPLOY ML MODELS
  15. 15. © 2019, Amazon Web Services, Inc. or its Affiliates. AWS IS FRAMEWORK AGNOSTIC Choose from popular frameworks Run them fully managed Or run them yourself
  16. 16. © 2019, Amazon Web Services, Inc. or its Affiliates. APACHE MXNET ON AWS Near-linear scalability up to 256 GPUs Dynamic training MXNet Model Server
  17. 17. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER TRAINING Amazon Simple Storage Service (S3) Bucket Training Data Amazon Simple Storage Service (S3) Bucket Model Artifacts BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN CONTAINER AWS MARKETPLACE Elastic Container Registry Training Config Instance Type Intance Count Container URI Hyperparameters etc. CreateTrainingJob() Amazon SageMaker Training ML Compute Instance Training Code ML Compute Instance Training Code ML Compute Instance Training Code … VPC ML Storage ML Storage ML Storage
  18. 18. © 2019, Amazon Web Services, Inc. or its Affiliates. AWS MARKETPLACE FOR MACHINE LEARNING Subscribe in a single click Available in Amazon SageMaker K E Y F E A T U R E S Automatic labeling via machine learning IP protection Automated billing and metering Browse or search AWS Marketplace S E L L E R S Broad selection of paid, free, and open-source algorithms and models Data protection B U Y E R S ML algorithms and models available instantly
  19. 19. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER REINFORCEMENT LEARNING Broad support for frameworks Broad support for simulation environments including SimuLink and MatLab K E Y F E A T U R E S TensorFlow, Apache MXNet, Intel Coach, and Ray RL support 2D & 3D physics environments and OpenAI Gym support Supports Amazon Sumerian and Amazon RoboMaker Fully managed Example notebooks and tutorials Reinforcement learning for every developer and data scientist
  20. 20. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER: BUILD, TRAIN AND DEPLOY ML MODELS
  21. 21. © 2019, Amazon Web Services, Inc. or its Affiliates. Amazon SageMaker Hyperparameter Tuning AMAZON SAGEMAKER HYPERPARAMETER TUNING Amazon Simple Storage Service (S3) Bucket Training Data Amazon Simple Storage Service (S3) Bucket Model Artifacts BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN CONTAINER AWS MARKETPLACE Elastic Container Registry Training Config Instance Type Intance Count Container URI Hyperparameters etc. CreateHyperParameterTuningJob() Tuning Config Objective Metric Parameter Ranges Resource Limits Strategy Bayesian Optimization Random Search Amazon SageMaker Training ML Compute Instance Training Code ML Compute Instance Training Code ML Compute Instance Training Code … VPC ML Storage ML Storage ML Storage
  22. 22. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER NEO K E Y F E A T U R E S Compiler & run-time are open source 1/10th the size of original models Train once, run anywhere with 2x the performance
  23. 23. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER: BUILD, TRAIN AND DEPLOY ML MODELS
  24. 24. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER HOSTING CreateModel() > CreateEndpointConfig() > CreateEndpoint() Amazon Simple Storage Service (S3) Bucket Model Artifacts Amazon SageMaker Hosting ML Compute Instance Model ML Compute Instance Model ML Compute Instance Model … BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN CONTAINER AWS MARKETPLACE Elastic Container Registry Hosting Config Instance Type Intance Count Container URI etc. HTTPS Endpoint Client Application Input Data (request) Inference (response) Endpoint Configuration VPC
  25. 25. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER BATCH TRANSFORM Amazon Simple Storage Service (S3) Bucket Input Data Amazon Simple Storage Service (S3) Bucket Results Amazon SageMaker Batch Transform … BUILT-IN ALGORITHMS BRING YOUR OWN SCRIPT BRING YOUR OWN CONTAINER AWS MARKETPLACE Elastic Container Registry CreateTransformJob() Transform Config Model Name Transform Resources Transform Output Batch Strategy etc. ML Compute Instance Model Agent Request Data Transformed Data ML Compute Instance Model Agent Request Data Transformed Data VPC
  26. 26. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON SAGEMAKER: BUILD, TRAIN AND DEPLOY ML MODELS
  27. 27. © 2019, Amazon Web Services, Inc. or its Affiliates. AUTOSCALING, A/B TESTING AND INFERENCE PIPELINES Amazon SageMaker Hosting HTTPS Endpoint Client Application Input Data (request) Inference (response) Endpoint Configuration ML Compute Instance Model 1 Model n … Pipeline Model Production Variant ML Compute Instance Model 1 Model n … Pipeline Model ML Compute Instance Model 1 V2 Model n V2 … Pipeline Model Production Variant VPC AWS Auto Scaling
  28. 28. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON ELASTIC INFERENCE Match capacity to demand Available between 1 to 32 TFLOPS K E Y F E A T U R E S Integrated with Amazon EC2, Amazon SageMaker, and Amazon DL AMIs Support for TensorFlow, Apache MXNet, and ONNX with PyTorch coming soon Single and mixed-precision operations Lower inference costs Reduce deep learning inference costs up to 75%
  29. 29. © 2019, Amazon Web Services, Inc. or its Affiliates. END-TO-END ML WORKFLOWS AND AUTOMATION Use end-to-end or pick what you need Automate with AWS Step Functions, Apache AirFlow or your own orchestrator
  30. 30. © 2019, Amazon Web Services, Inc. or its Affiliates. HOW TO USE AMAZON SAGEMAKER AWS CLI AWS Console AWS SDKs SageMaker Python SDK
  31. 31. © 2019, Amazon Web Services, Inc. or its Affiliates.© 2019, Amazon Web Services, Inc. or its Affiliates. DEMO
  32. 32. © 2019, Amazon Web Services, Inc. or its Affiliates.© 2019, Amazon Web Services, Inc. or its Affiliates. WAYS TO GET STARTED
  33. 33. © 2019, Amazon Web Services, Inc. or its Affiliates. ONLINE RESOURCES Amazon SageMaker landing page https://aws.amazon.com/sagemaker/ Amazon SageMaker documentation https://docs.aws.amazon.com/sagemaker/index.html SageMaker Python SDK https://github.com/aws/sagemaker-python-sdk SageMaker MXNet Container https://github.com/aws/sagemaker-mxnet-container SageMaker Tensorflow Container https://github.com/aws/sagemaker-tensorflow-container SageMaker PyTorch Container https://github.com/aws/sagemaker-pytorch-container SageMaker Chainer Container https://github.com/aws/sagemaker-chainer-container SageMaker Scikit-learn Container https://github.com/aws/sagemaker-scikit-learn-container SageMaker Containers https://github.com/aws/sagemaker-containers Amazon SageMaker Examples https://github.com/awslabs/amazon-sagemaker-examples Amazon SageMaker Workshop https://github.com/awslabs/amazon-sagemaker-workshop
  34. 34. © 2019, Amazon Web Services, Inc. or its Affiliates. MACHINE LEARNING UNIVERSITY Uses the same materials used to train Amazon developers Foundational knowledge with real-world application Structured courses and specialist certification https://aws.amazon.com/training/learning-paths/machine-learning/
  35. 35. © 2019, Amazon Web Services, Inc. or its Affiliates. AMAZON ML SOLUTIONS LAB Brainstorming Modeling Teaching Leverage Amazon experts with decades of ML experience with technologies like Amazon Echo, Amazon Alexa, Prime Air and Amazon Go Amazon ML Solutions Lab provides ML expertise Quick turnaround Demonstration of functionality Static data Limited live data-sets Core functionality Limited integration with production systems Initial Architecture Full live data-sets Enhanced functionality Link to production systems Scaled Architecture Iteration on MVP2 Fully operationally Fully supported Live security-approved account https://aws.amazon.com/ml-solutions-lab/
  36. 36. © 2019, Amazon Web Services, Inc. or its Affiliates. Artificial Intelligence. Get Started. Serie webinar in Italiano Today 27 Giugno25 Giugno 11 Giugno
  37. 37. © 2019, Amazon Web Services, Inc. or its Affiliates. Thank You Giuseppe Angelo Porcelli Principal Solutions Architect Amazon Web Services EMEA

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