Machine Learning at Amazon: A Long Heritage
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Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
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Machine Learning at Amazon: A Long Heritage
- 1. @dmbanga@dmbanga Dan R. Mbanga Global Lead BD Mgr – Amazon AI Platforms Machine Learning on AWS
- 3. @dmbanga Tens of thousands of active developers running ML on AWS 250% growth YoY 8 out of 10 of all ML workloads run on AWS* *TENSORFLOW ON AWS Nucleus research December 2017 - Report R206
- 4. @dmbanga Tens of thousands of customers running Machine Learning on AWS
- 6. @dmbanga • Applied Research • Core Research • Alexa • Demand Forecasting • Risk Analytics • Search • Recommendations • AI Services • Q&A Systems • Supply Chain Optimization • Advertising • Machine Translation • Video Content Analysis • Robotics • Lots of Computer Vision.. • NLP / NLU Machine Learning at Amazon: A long heritage
- 7. @dmbanga ML @ AWS OUR MISSION Put machine learning in the hands of every developer and data scientist
- 8. @dmbanga Accessibility Reproducibility Momentum … all of it in motion! scale * speed ML frameworks Pre-trained APIs GPUs Data + catalog Fined-grained access control SDKs, Libraries ML examples Templates Packaging (algos, infra, profiles, data)
- 9. @dmbanga FRAMEWORKS KERAS The AWS Machine Learning Stackml.aws
- 10. @dmbanga FRAMEWORKS KERAS PLATFORMS Amazon SageMaker AWS DeepLens The AWS Machine Learning Stackml.aws
- 11. @dmbanga FRAMEWORKS KERAS PLATFORMS AI SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O 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 X Amazon SageMaker AWS DeepLens The AWS Machine Learning Stackml.aws
- 12. @dmbanga AI SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O 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 X The AWS Machine Learning Stackml.aws
- 13. @dmbanga Amazon ML Services Bringing it together: Multilanguage product analytics Twitter Stream API S3 AthenaKinesis Translate Lambda Comprehend Transcribe
- 14. @dmbanga Amazon Lex Scale Business Logic SecurityAnalytics Text to Speech Speech to Intent End to End Native support & maintains context One-click deployment Completely managed service Native integration with AWS Lambda Encrypted data in transit & at rest Monitor and improve Amazon Polly integrated into API ASR + NLU integrated into one API Dialog Management Deployment
- 15. @dmbanga Amazon Lex Use Cases Informational Bots Chatbots for everyday consumer requests Application Bots Build powerful interfaces to mobile applications News updates Weather information Game scores …. Book tickets Order food Manage bank accounts …. Enterprise Productivity Bots Streamline enterprise work activities and improve efficiencies Check sales numbers Marketing performance Inventory status …. Internet of Things (IoT) Bots Enable conversational interfaces for device interactions Wearables Appliances Auto …. Contact Center Bots Chatbots for customer service IVR Account inquiries Bill payment Service update ….
- 16. @dmbanga @dmbanga That sounds good, Dan, but how do I get insights from my own data?
- 17. @dmbanga B U IL D Where should you spend your time? Design ML training experiments
- 18. @dmbanga B U IL D TRA IN Where should you spend your time? Run scalable training
- 19. @dmbanga B U IL D TRA IN Where should you spend your time? Tune Hyperparameters TU N E
- 20. @dmbanga B U IL D TRA IN DEPL O Y Where should you spend your time? TU N E Deploy and operate
- 21. @dmbanga The AWS Machine Learning Platform FRAMEWORKS KERAS PLATFORMS Amazon SageMaker AWS DeepLens NVIDIA Tesla V100 GPUs (14x faster than P2) P3 Machine Learning AMIs 5,120 Tensor cores 128GB of memory 1 Petaflop of compute NVLink 2.0 I N F R A S T R U C T U R E API For the developer, data scientist, AI researcher, or any ML enthusiast
- 22. @dmbanga AWS has shipped over 100 new services and major new features for machine learning since re:Invent 2017 Amazon Rekognition Image and Video launch in US East (Ohio) Advanced Indexing Support Amazon Lex region support Amazon Lex: Default Responses Amazon Lex: DTMF Support Amazon Polly WordPress plugin integrates Translate Amazon Rekognition Image and Video launch in Tokyo and Sydney Amazon Transcribe - GA Amazon Translate - GA Anomaly Detection (Random Cut Forest) Algorithm Automate role creation during set up Automatic Model Tuning Autoscaling console AWS DeepLens devices available for sale BlazingText Algorithm Built-in Algorithms Pipe Mode Support Caffe to MXNet code translator Chainer pre-built container CloudFormation support CloudTrail integration for audit logs CUDA 9.0 and 9.1 + cuDNN 7.1.4 + GPU Driver 390.46 + NCCL 2.2.13 upgrade Customer VPC support for training and hosting DCA: AWS Deep Learning AMI in DCA Deep AR algorithm Deep Learning AMI in GovCloud Deep Learning AMI: Add Chainer , MXNet 1.1 support (incl. BJS) Deep Learning AMI: EC2 Optimized Binaries for TensorFlo w Deep Learning AMI: Optimized Chainer 4, CNTK 2.5 Deep Learning AMI: Refresh for EC2 optimized TensorFlo w 1.8 Deep Learning AMI: Refresh with TF 1.5 and MXNet 1.1 Deep Learning AMI: Regional Expansion in 5 public regions: US West (California), Canada, Sao Paulo, London, and Paris Deep Learning AMI: Release latest DL AMI version in BJS DL AMI launch in BJS and FRA,BOM, SIN Deep Learning AMI: TensorFlo w multi - gpu optimizations using Horovod , NCCL and OpenMPI Deep Learning AMI: TF 1.5 RC0, MMS, TF Serving, TensorBoard (including BJS) FP16 support DL AMI refresh with MXNet 1.0, TF for CUDA 9 and PyTorch for CUDA 9 DeepLens integration with Amazon Kinesis video stream Export/Import Amazon Lex bot schemaFace Recognition v3 launch GDPR Compliance Gradient Compression HIPAA compliance Import from SageMaker to DeepLens Increase Character Limit Internet- free notebook instances KMS support for training and hosting Linear Learner Improvements - Early Stopping, New Loss Functions, and Class Weights ml.p3.2xl arg e notebook instances Model Optimizer More instance types support MXNet Model Server MXNet Model Server MXNet Model Server v0.2 MXNet Model Server v0.3 MXNet v1.0 release MXNet v2.0 release nbexample support in SageMaker notebook instances NCCL support NER, Keyphrase, Sentiment Batch New Breath SSML tag New French Voice (female) New Phonation SSML tag Notebook bootstrap script ONNX v1.0 announcemen t PCI DSS Compliance Polly in GovCloud Polly voices in Alexa Skills PrivateLink support for SageMaker inferencing APIs PyTorch pre- built container Refresh DLAMI in DCA Region expansion to FRA Region expansion to NRT Region expansion to Sydney Rekognition HIPAA support Rekognition Video in AWS Gov Cloud SageMaker region expansion to ICN Store transcription output in customer S3 buckets Support for Keras 2.0 Support Gluon models Tag-based access control TensorFlo w 1.5, MXNet 1.0, and CUDA 9 Support TensorFlo w 1.6 and MXNet 1.1 Containers TensorFlo w 1.7 Containers TensorFlo w 1.8 Container Tensorflow and Caffe support TensorFlo w and MXNet Containers - Open Sourcing and Local Mode TrainingJob cloning in console Transcribe integration with CloudTrail Transcribe integration with CloudWatch Amazon Polly WordPress Plugin Translate -- add to Polly WordPress plugin Translate -- GDPR compliance Translate -- Gov Cloud Translate -- Mobile SDK XGBoost Instance Weights
- 23. @dmbanga
- 24. @dmbanga Pre-built notebook examples Built-in, high performance algorithms TRA IN DEPL O Y B U IL D Build, train, tune, and host your own models TU N E Amazon SageMaker
- 25. @dmbanga Pre-built notebook examples Built-in, high performance algorithms One-click training Hyperparameter optimization DEPL O Y B U IL D TRA IN & TU N E Build, train, tune, and host your own models Amazon SageMaker
- 26. @dmbanga Fully managed hosting with auto- scaling One-click deployment DEPL O Y Pre-built notebook examples Built-in, high performance algorithms One-click training Hyperparameter optimization B U IL D TRA IN & TU N E Build, train, tune, and host your own models Amazon SageMaker
- 27. @dmbanga Fully managed hosting with auto- scaling One-click deployment Pre-built notebook examples Built-in, high performance algorithms One-click training B U IL D TRA IN & TU N E DEPL O Y Hyperparameter optimization End-to-end encryption End-to-end VPC support Compliance and audit capabilities Metadata and experiment management capabilities Pay as you go Build, train, tune, and host your own models Amazon SageMaker
- 28. @dmbanga N E W
- 29. @dmbanga Accessibility Reproducibility Momentum SDKs, Libraries ML examples Templates Packaging (algos, infra, profiles, data)
- 30. @dmbanga Rethinking Algorithms Design for large scale & streaming data AlgorithmData Model Amazon SageMaker
- 31. @dmbanga Rethinking Algorithms Design for large scale & streaming data AlgorithmData Model N EW DA TA PREDICTION Amazon SageMaker
- 32. @dmbanga Rethinking Algorithms Design for large scale & streaming data XGBoostCustomers activity Model NE W C US T O ME R SEGMENTS Amazon SageMaker
- 33. @dmbanga Rethinking Algorithms Design for large scale & streaming data Factorization Machine Users profiles x Purchase history Model NE W US E R RECOMMENDED PRODUCTS Amazon SageMaker
- 34. @dmbanga XGBoost, KNN, KMeans, Multi-class classifiers Anomaly detection PCA Sequence modeling Deep forecasting Bring your own algorithm Recommender systems Regressions Image classification Object detection Text classification Topic modeling Algorithms as-a-service + open source Amazon SageMaker Rethinking Algorithms Design for large scale & streaming data Amazon SageMaker
- 35. @dmbanga Rethinking Algorithms Design for large scale & streaming data Amazon SageMaker Object detection Multi-class classifiers Deep forecasting Text classification Anomaly detection PCA, kNN, regression, boosted trees… Designed to be 10X better Streaming data Single pass training Checkpointing Available as an API 10x API
- 36. @dmbanga
- 37. @dmbanga “Hmmm… How can I make my own algorithms as fast as those in SageMaker?”
- 38. @dmbanga Accelerate Your Own Algorithms By Streaming Large Volumes of Training Data From Amazon S3 A m a z o n S a g e M a k e r S t r e a m i n g A l g o r i t h m s N E W
- 39. @dmbanga SageMaker Streaming For Custom Algorithms Faster training Stream data to your own algorithm Quicker time to start training Lower cost training N E W Amazon SageMaker
- 40. @dmbanga SageMaker Streaming For Custom Algorithms Faster training Stream data to your own algorithm Quicker time to start training Lower cost training N E W Amazon SageMaker Additional frameworks coming soon TensorFlow
- 41. @dmbanga N E W
- 42. @dmbanga Accessibility Reproducibility Momentum … all of it in motion! scale * speed
- 43. @dmbanga KERAS Optimize your models Host models A/B testTrain with SageMaker algorithms Train with your own algorithms Amazon SageMaker Custom ML Models Using Your Own Data
- 44. @dmbanga KERAS Host models A/B test Amazon SageMaker Custom ML Models Using Your Own Data
- 45. @dmbanga Low latency Easy to integrate API endpoint Auto-scaling Fault tolerant, multi-AZ Amazon SageMaker Elastic Hosting for Custom Models
- 46. @dmbanga Low latency Easy to integrate API endpoint Auto-scaling Fault tolerant, multi-AZ What if your files are big? What if you need to batch process? B u t …
- 47. @dmbanga Run Fully-managed, High Throughput Batch Transform Jobs with a Simple API Call A m a z o n S a g e M a k e r B a t c h T r a n s f o r m N E W
- 48. @dmbanga Batch test models before hosting Process data dumps in a batch Process large files more easily Test models before deployment at the edge Amazon SageMaker Batch Processing
- 49. @dmbanga Batch test models before hosting Process data dumps in a batch Process large files more easily Test models before deployment at the edge Reuse pre-processing pipelines between training and prediction Use same model for batch and real-time Fully managed batch transforms Amazon SageMaker Batch Processing
- 50. @dmbanga Deployment time down by 90% with Amazon SageMaker 6 MONTHS < 1 WEEK Amazon SageMaker Momentum…
- 51. @dmbanga @dmbanga How do we put ML in the hands of developers (literally)? @dmbanga
- 52. @dmbanga@dmbanga AWS DeepLens The world’s first deep-learning enabled video camera for developers
- 53. @dmbanga @dmbanga 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 Amazon ML Solutions Lab
- 54. @dmbanga @dmbanga Custome r S ucce ss With Amaz on ML S olutions Lab
- 55. @dmbanga @dmbanga “Dan, I am building a ML organization…”
- 56. @dmbanga @dmbanga Machine Learning is inherently Interdisciplinary. Physical Scientists Analysts Architects/ Integrators Computer Scientists Organizing Your People for Success
- 57. @dmbanga @dmbanga Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data. Organizing Your People for Success Machine Learning is inherently Interdisciplinary.
- 58. @dmbanga @dmbanga Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data. Understand Business Problem Framing Organizing Your People for Success Machine Learning is inherently Interdisciplinary.
- 59. @dmbanga @dmbanga Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data. Understand Business Problem Framing Understand Computer Logic and ML Science Organizing Your People for Success Machine Learning is inherently Interdisciplinary.
- 60. @dmbanga @dmbanga Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data. Understand Business Problem Framing Understand Computer Logic and ML Science Can put everything together Organizing Your People for Success Machine Learning is inherently Interdisciplinary.
- 61. @dmbanga @dmbanga Create a positive work environment for ML talent Centralize new technologies Attract and motivate the right talent Organizing Your People for Success Machine Learning is inherently Interdisciplinary.
- 62. @dmbanga @dmbanga ML Consulting Competency Partners
- 63. @dmbanga @dmbanga Data Lake Storage Amazon S3 Security Access Control Encryption VPC KMS Auditing Compliance Roles Fine Grained Access Controls Compute Powerful GPU & CPU Instances AWS Lambda Analytics Amazon Athena Amazon EMR Amazon Redshift & Redshift Spectrum