A M E W O R K S & I N F R A S T R U C T U R E The Amazon ML Stack 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 C O M P R E H E N D M E D I C A L L E X R E K O G N I T I O N V I D E O Vision Speech Chatbots 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 F O R E C A S T T E X T R A C T P E R S O N A L I Z E 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 M L S E R V I C E 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 Models without training data (REINFORCEMENT LEARNING) Algorithms & models ( A W S M A R K E T P L A C E ) Language Forecasting Recommendations NEW NEW NEW NEW NEW NEW NEW NEW NEW RL Coach
instantly K E Y F E A T U R E S Automatic labeling via machine learning IP protection Automated billing and metering S E L L E R S Broad selection of paid, free, and open-source algorithms and models Data protection Discoverable on your AWS bill B U Y E R S
rights reserved. S U M M I T The best place to run TensorFlow Fastest time for TensorFlow 65% 90% 30m 14m • 85% of TensorFlow workloads in the cloud runs on AWS (2018 Nucleus report) • Available w/ Amazon SageMaker and the AWS Deep Learning AMIs
rights reserved. S U M M I T Amazon Elastic Inference Reduce deep learning inference costs up to 75% K E Y F E A T U R E S Integrated with Amazon EC2 and Amazon SageMaker Support for TensorFlow, Apache MXNet - PyTorch coming soon Single and mixed-precision operations
• Get data to notebooks securely Training • Setup and manage clusters • Scale/distribute ML algorithms Deployment • Setup and manage inference clusters • Manage and auto scale inference APIs • Testing, versioning, and monitoring Fetch data Clean & format data Prepare & transform data Train model Evaluate model Integrate with prod Monitor/ debug/refresh 6–18 months Data Wrangling • Manage data ingestion • Execute ETL
Pre-built notebooks for common problems Built-in, high performance algorithms One-click training B U I L D T R A I N & T U N E D E P L O Y Hyperparameter optimization
an input to an output based on example input-output pairs» «The task of learning a function that maps an input to an output based on example input-output pairs»
rights reserved. S U M M I T AWS DeepRacer Car Specifications CAR 18th scale 4WD with monster truck chassis CPU Intel Atom™ Processor MEMORY 4GB RAM STORAGE 32GB (expandable) WI-FI 802.11ac CAMERA 4 MP camera with MJPEG DRIVE BATTERY 7.4V/1100mAh lithium polymer COMPUTE BATTERY 13600mAh USB-C PD SENSORS Integrated accelerometer and gyroscope PORTS 4x USB-A, 1x USB-C, 1x Micro-USB, 1x HDMI SOFTWARE Ubuntu OS 16.04.3 LTS, Intel® OpenVINO™ toolkit, ROS Kinetic
Winners of each DRL Race and top points getters compete in Championship Cup at re:Invent 2019 Virtual tournaments through the year AWS DeepRacer League World’s first global autonomous racing league, open to anyone