© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Become a machine learning developer using AWS Machine Learning Services Alex Casalboni Technical Evangelist, AWS

S U M M I T About me • Software Engineer & Web Developer • Data science background • Worked in a startup for 4.5 years • AWS Customer since 2013

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Put machine learning in the hands of every developer Our mission at AWS

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T The picture can't be displayed. Some of our machine learning customers…

S U M M I T 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 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

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Machine Learning Training & Certification AWS DeepRacer AWS DeepLens Amazon SageMaker

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Over 230 algorithms and models that can be deployed directly to Amazon SageMaker

AWS Marketplace for Machine Learning ML algorithms and models available 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

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Pre-configured environments to quickly build deep learning applications

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T AWS is framework agnostic Choose from popular frameworks Run them fully managed Or run them yourself

© 2019, Amazon Web Services, Inc. or its affiliates. All 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

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Reduce deep learning inference costs up to 75%

© 2019, Amazon Web Services, Inc. or its affiliates. All 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

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

Machine Learning Life Cycle Experimentation • Setup and manage Notebooks • 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

Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment 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

Amazon SageMaker algorithms BlazingText DeepAR Forecasting Image Classification Object Detection IP Insights K-Means & K-Nearest Neighbors Latent Dirichlet Allocation (LDA) Principal Component Analysis (PCA) Linear Learner Neural Topic Model (NTM) Factorization Machines Object2Vec Random Cut Forest (RCF) Semantic Segmentation Sequence-to-Sequence XGBoost docs.aws.amazon.com/sagemaker/latest/dg/algos.html

But what happens before building and training a model?

Types of Machine Learning Reinforcement learning Supervised learning Unsupervised learning AMOUNT OF DATA SOPHISTICATION Supervised learning

Supervised Learning «The task of learning a function that maps 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»

Public datasets on the web registry.opendata.aws (100+, already on Amazon S3) github.com/awesomedata/awesome-public-datasets (600+) archive.ics.uci.edu (400+) www.data.gov (200k+) openneuro.org (200+)

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Build highly accurate training datasets and reduce data labeling costs by up to 70%

Data in S3 Mechanical Turk (public) Your own employees (private) Third-party labelers (vendor) Human annotations Training data How it works (1) What if we have 1M+ images to annotate?

Data in S3 Automatic annotations Human annotations Training data Active Learning model >80% confidence <80% confidence How it works (2) Goal: reduce data labeling costs by up to 70%

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Types of Machine Learning Reinforcement learning Supervised learning Unsupervised learning AMOUNT OF DATA SOPHISTICATION Reinforcement learning

Reinforcement Learning Use Cases AUTONOMOUS CARS FINANCIAL TRADING DATACENTER COOLING FLEET LOGISTICS

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

© 2019, Amazon Web Services, Inc. or its affiliates. All 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

3D simulator with virtual track Reward function Reinforcement learning algorithm How does it work?

Build reinforcement learning model DeepRacer League Races at AWS Summits 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

AWS DeepRacer League World’s first global autonomous racing league, open to anyone aws.amazon.com/deepracer/league/points-and-prizes/

Rf(x) { "all_wheels_on_track": bool, "x": float, "y": float, "distance_from_center": float, "is_left_of_center": bool, "heading": float, "progress": float, "steps": int, "speed": float, "steering_angle": float, "track_width": float, "waypoints": [[float, float], … ], "closest_waypoints": [int, int] }

Rf(x) def reward_function(params): # Read input parameters track_width = params['track_width’] distance_from_center = params['distance_from_center’] all_wheels_on_track = params['all_wheels_on_track’] speed = params['speed'] marker = 0.2 * track_width if not all_wheels_on_track: reward = 0.001 else: if distance_from_center <= marker: reward = 1.0 else: reward = 0.1 reward *= speed return float(reward)

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ai.aws & ml.aws aws.training/machinelearning github.com/awslabs/amazon-sagemaker-examples

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Alex Casalboni Technical Evangelist, AWS

S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.