Introducing Amazon SageMaker
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This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
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Introducing Amazon SageMaker
- 1. Introduction to Amazon SageMaker Brent Rabowsky, Solutions Architect © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 2. Agenda • Why did we build Amazon SageMaker? • What is Amazon SageMaker? • How do I get started using Amazon SageMaker? • Amazon SageMaker customer use cases • How Amazon SageMaker works with other AWS AI Services • Q&A
- 3. Why did we build Amazon SageMaker?
- 4. Data is part of the fabric of applications Frontend and UX Mobile Backend and operations Data and analytics
- 5. Three types of data-driven development Retrospective analysis and reporting Here-and-now real-time processing and dashboards Inferences to enable smart applications Amazon Kinesis Amazon EC2 AWS Lambda Amazon Redshift Amazon RDS Amazon S3 Amazon EMR Amazon Deep Learning AMI Amazon Machine Learning Amazon SageMaker
- 6. Machine Learning Process is Hard… Fetch data Clean & format data Prepare & transform data Train model Evaluate model Integrate with prod Monitor / debug / refresh Data wrangling • Set up and manage Notebook environments • Get data to notebooks securely Experimentation • Setup and manage clusters • Scale/distribute ML algorithms Deployment • Setup and manage inference clusters • Manage and auto scale inference APIs • Testing, versioning, and monitoring
- 7. Machine Learning Process is Hard… Fetch data Clean & format data Prepare & transform data Train model Evaluate model Integrate with prod Monitor / debug / refresh Data wrangling • Set up and manage Notebook environments • Get data to notebooks securely Experimentation • Set up and manage clusters • Scale/distribute ML algorithms Deployment • Setup and manage inference clusters • Manage and auto scale inference APIs • Testing, versioning, and monitoring
- 8. Machine Learning Process is Hard… Fetch data Clean & format data Prepare & transform data Train model Evaluate model Integrate with prod Monitor / debug / refresh Data wrangling • Set up and manage Notebook environments • Get data to notebooks securely Experimentation • Set up and manage clusters • Scale/distribute ML algorithms Deployment • Set up and manage inference clusters • Manage and auto scale inference APIs • Testing, versioning, and monitoring
- 9. … and time consuming ... Fetch data Clean & format data Prepare & transform data Train model Evaluate model Integrate with prod Monitor / debug / refresh 6-18 months
- 10. … but full of potential ”Machine learning and AI is a horizontal enabling layer. It will empower and improve every business, every government organization, every philanthropy — basically there’s no institution in the world that cannot be improved with machine learning… We’re in a great position, because of the success of Amazon Web Services, to be able to put energy into making those techniques easy and accessible. ” --Jeff Bezos
- 11. What is Amazon SageMaker?
- 12. A managed service that provides the quickest and easiest way for your data scientists and developers to get ML models from idea to production. Amazon SageMaker
- 13. End-to-end Machine Learning Platform Zero setup Flexible model training Pay by the second Introducing Amazon SageMaker
- 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon SageMaker’s Four Components: 1 2 3 4 I I I I Notebook Instances Algorithms ML Training Service ML Hosting Service
- 15. Resizable as you need Common tools pre-installed Easy access to your data sources No servers to manage 1) Zero setup for data exploration “Just add data”
- 16. Streaming datasets, for cheaper training Train faster, in a single pass Greater reliability on extremely large datasets Choice of several ML algorithms 2) Algorithms designed for huge datasets
- 17. XGBoost, FM, and Linear for classification and regression Kmeans and PCA for clustering and dimensionality reduction Image classification with convolutional neural networks LDA and NTM for topic modeling, seq2seq for translation More than just general purpose algorithms
- 18. New: Time Series Forecasting with DeepAR Input Network 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
- 19. Amazon- optimized algorithms using the AWS SDK… … or Apache Spark SageMaker Estimators Bring your own deep learning script… … or your custom algorithm Docker image 3) Distributed training that works with you
- 20. One step deployment Low latency, high throughput, and high reliability A/B testing Use your own model 4) Quickly deploy in production
- 21. Modular ar c hitec ture s o you c an us e w hat you need Past Data Training algorithm Model artifacts Inference code Client application Model Data Inference Ground truth Amazon SageMaker
- 22. ML compute by the second starting at $0.0464/hr ML storage by the second at $0.14 per GB-month Data processed in notebooks and hosting at $0.016 per GB Free trial to get started quickly Pay as you go and inexpensive
- 23. How do you get started using Amazon SageMaker?
- 24. Start with notebook samples
- 25. Modify to access your data sources
- 26. Train your model
- 29. Amazon SageMaker Use Cases
- 30. Amazon SageMaker: Launch Customer
- 31. Some AI/ML use cases at Intuit: Customer Care and Expert Advice Fraud Detection and Prevention Smart Products
- 32. Designed to keep fraudsters out of systems and data Strive to stay several moves ahead of them by leveraging machine learning-generated insights from data Near real-time fraud detection in TurboTax: • Account take-over detection • Identity theft detection
- 33. Model Hosting (Amazon SageMaker) Near real-time fraud detection in AWS using Amazon SageMaker Calculate Features Reader Cleanser Processor Data Lookup Training Feature Store Model Training (Amazon SageMaker) Model Client Service
- 34. Key benefits of Amazon SageMaker @ Intuit Ad hoc setup and management of notebook environments Limited choices for model deployment Competing compute resources across teams Easy data exploration in Amazon SageMaker notebooks Building around virtualization for flexibility Auto-scalable model hosting environment From To
- 35. Amazon SageMaker: Launch Customer “As the world’s leading provider of high-resolution Earth imagery, data and analysis, DigitalGlobe works with enormous amounts of data every day. DigitalGlobe is making it easier for people to find, access, and run compute against our entire 100PB image library, which is stored in AWS’s cloud, to apply deep learning to satellite imagery. We plan to use Amazon SageMaker to train models against petabytes of Earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe's Geospatial Big Data Platform (GBDX) users can just push a button, create a model, and deploy it all within one scalable distributed environment at scale. ” - Dr. Walter Scott, CTO of Maxar Technologies and founder of DigitalGlobe
- 36. Amazon SageMaker: Launch Customer “We’re focused on making it faster and easier than ever to hire and get hired, training our machine learning algorithms against hundreds of millions of historical transactional activities in order to deliver highly relevant job matches as quickly as possible. Amazon SageMaker provided us with an answer to problems we had with ML workflow management, allowing us to train, evaluate and deploy models in a flexible way. In addition, Amazon SageMaker's modularity provides the ability to build and create models independently, which is a compelling feature for ZipRecruiter. ” - Avi Golan, VP of Engineering, ZipRecruiter
- 37. How Amazon SageMaker works with other AWS AI Services
- 38. The Amazon machine learning stack PLATFORM SERVICES APPLICATION SERVICES FRAMEWORKS & INTERFACES Caffe2 CNTK Apache MXNet PyTorch TensorFlo w Torch Keras Gluon AWS Deep Learning AMIs Amazon SageMaker AWS DeepLens Rekognition Transcribe Translate Polly Comprehend Lex Amazon Mechanical Turk Amazon ML
- 39. Amazon EC2 P3 Instances The fastest, most powerful GPU instances in the cloud • Up to eight NVIDIA Tesla V100 GPUs • 1 PetaFLOP of computational performance – 14x better than P2 • 300 GB/s GPU-to-GPU communication (NVLink) – 9X better than P2 • 16GB GPU memory with 900 GB/sec peak GPU memory bandwidth
- 40. Q&A (at the Ask an Architect bar)