AI/ML on AWS By Ahmed Raafat

COMMUNITY DAY MENA Machine Learning on AWS

Ahmed Raafat Senior Solutions Architect-AWS

All rights reserved | M A C H I N E L E A R N I N G I S H A P P E N I N G I N C O M P A N I E S O F E V E R Y S I Z E A N D I N D U S T R Y Tens of thousands customers have chosen AWS for their ML workloads | More than twice as many customers using ML than any other cloud provider

All rights reserved | W H Y A W S F O R M L ? 200+ new features and services launched this last year alone Solutions for everyone from ML scientists to application developers Support all three of the major frameworks Broadest and deepest set of AI and ML services Single IDE for the entire ML workflow At least 54% lower TCO Up to 70% cost reduction in data-labeling Up to 90% cost reduction with managed spot training Accelerate your adoption of ML with SageMaker Built on the most comprehensive cloud platform Highly secure, reliable, fully featured data store The strongest set of compute, storage, security, database, and analytics capabilities to build upon 85% TensorFlow in the cloud runs on AWS

All rights reserved | 12 © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved | Technology Bringing ML into your digital transformation requires a new “stack” that makes it easier to put ML to work

All rights reserved | The AWS ML Stack Broadest and most complete set of Machine Learning capabilities VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Ground Truth AWS Marketplace for ML Neo Augmented AI Built-in algorithms Notebooks Experiments Processing Model training & tuning Debugger Autopilot Model hosting Model Monitor Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE Amazon SageMaker DeepGraphLibrary

All rights reserved | Amazon Rekognition Easy-to-use deep-learning based computer vision analysis Some Amazon Rekognition customers Use case example: video search index Rekognition Video Rekognition Image Text in Video

All rights reserved | Amazon Rekognition Custom Labels Identify objects and scenes in images that are specific to your business needs Machine: 96.9% Wheel: 95.5% Bracket: 80.3% Prop shaft mid bearing Clutch pressure plate Plant: 99.2% Corn: 95.3% Food: 95.3% Vegetable: 95.3% Sweet Corn Field Corn General identification Specialized identification (Custom Labels)

All rights reserved | Amazon Rekognition Sample APIs and resources Amazon Rekognition (+ Custom Labels) Amazon Rekognition Image CompareFaces CreateCollection DeleteCollection DeleteFaces DescribeCollection DetectFaces DetectLabels DetectModerationLabels DetectText GetCelebrityInfo IndexFaces ListCollections ListFaces RecognizeCelebrities SearchFaces SearchFacesByImage Amazon Rekognition Custom Labels CreateProject CreateProjectVersion DescribeProjects DescribeProjectVersions DetectCustomLabels StartProjectVersion StopProjectVersion Amazon Rekognition Video Stored Video GetCelebrityRecognition GetContentModeration GetFaceDetection GetFaceSearch GetLabelDetection GetPersonTracking StartCelebrityRecognition StartContentModeration StartFaceDetection StartFaceSearch StartLabelDetection StartPersonTracking Amazon Rekognition Video Streaming Video CreateStreamProcessor DeleteStreamProcessor DescribeStreamProcessor ListStreamProcessors StartStreamProcessor StopStreamProcessor aws rekognition detect-labels –image \ '{"S3Object":{"Bucket":"bucket","Name":"image"}}' DetectLabels Request: { "Image": { "Bytes": blob, "S3Object": { "Bucket": "string", "Name": "string", "Version": "string" } }, "MaxLabels": number, "MinConfidence": number } Response: { "LabelModelVersion": "string", "Labels": [ { "Confidence": number, "Instances": [ { "BoundingBox": { "Height": number, "Left": number, "Top": number, "Width": number }, "Confidence": number } ], "Name": "string", "Parents": [ { "Name": "string" } ] } ], "OrientationCorrection": "string" } Some sample calls… Check the documentation: https://docs.aws.amazon.com/rekognition/ AWS SDK available for: C++, Go, Java, JavaScript, .NET, Node.js, PHP, Python, Ruby

All rights reserved | 20 © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved | Accurate time-series forecasting service, based on the same technology used at Amazon.com. No ML experience required. Amazon Forecast

All rights reserved | Amazon Forecast Improve forecasting accuracy by up to 50% at 1/10th the cost K E Y F E A T U R E S Consider multiple time-series at once Automatic machine learning Visualize forecasts & import results into business apps Evaluate model accuracy Schedule forecasts and model retraining Bring existing algorithms from Amazon SageMaker Privacy & encryption

All rights reserved | M O D E R N I Z E Y O U R C O N T A C T C E N T E R T O I M P R O V E C U S T O M E R S E R V I C E Voice of the customer analytics | Automated service agents | Multi-lingual text support Workforce forecasting and agent analysis | Next best action recommendation POLLY TRANSCRIBE TRANSLATE COMPREHEND LEX PERSONALIZE

All rights reserved | A U T O M A T E M E D I A W O R K F L O W S T O R E D U C E C O S T S A N D M O N E T I Z E C O N T E N T Media metadata tagging | Highlight clipping | Subtitling and localization | Content moderation | Compliance | Contextual ad insertion REKOGNITION IMAGE REKOGNITION VIDEO COMPREHEND TRANSCRIBE TRANSLATE TEXTRACT SAGEMAKER

All rights reserved | R E D U C E L O C A L I Z A T I O N C O S T S A N D I M P R O V E A C C U R A C Y POLLY TRANSCRIBE TRANSLATE COMPREHEND Website & document translation | Recorded call analysis | Video subtitling | Accessibility

All rights reserved | • Fully-managed training and hosting • Near-linear scaling across 100s of GPU • 75% lower inference costs with Amazon Elastic Inference • 3x faster network throughput with EC2 P3 T H E B E S T P L A C E T O R U N T E N S O R F L O W Amazon SageMaker is the best place to run TensorFlow in the cloud 65% Stock TensorFlow AWS-optimized TensorFlow 90%

All rights reserved | The machine learning workflow is iterative and complex Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Challenging ?!

All rights reserved | Fully managed data processing jobs and data labeling workflows One-click collaborative notebooks and built- in, high performance algorithms and models One-click training Debugging and optimization One-click deployment and autoscaling Amazon SageMaker helps you build, train, and deploy models Visually track and compare experiments Automatically spot concept drift Fully managed with auto-scaling for 75% less Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Collect and prepare training data Choose or bring your own ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Add human review of predictions Web-based IDE for machine learning Automatically build and train models

All rights reserved | Amazon SageMaker Notebooks Access your notebooks in seconds Administrators manage access and permissions Share notebooks with a single click Dial up or down compute resources (coming soon) Start your notebooks without spinning up compute resources Fast-start sharable notebooks (in preview)

All rights reserved | Classification Computer Vision Topic Modeling Working with Text Recommendation Forecasting • Linear Learner • XGBoost • KNN • Image Classification • BlazingText • Supervised • Unsupervised • Factorization Machines • DeepAR • LDA • NTM Amazon SageMaker has built-in algorithms or bring your own Anomaly Detection • Random Cut Forests Sequence Translation • Seq2Seq • Object Detection Clustering • KMeans Feature Reduction • PCA Regression • Linear Learner • XGBoost • KNN • IP Insights • Semantic Segmentation • Object2Vec

All rights reserved | You can shop for algorithms, models, and data in AWS Marketplace for ML AWS Marketplace for Machine Learning Browse or search AWS Marketplace Subscribe in a single click Available in Amazon SageMaker

All rights reserved | Hundreds of algorithms, models, and data Natural language processing Text-to-speech Object detection Speech recognition Grammar and parsing Text generation Speaker identification Regression Text OCR Text classification Text clustering Computer vision 3D images Handwriting recognition Named entity recognition Anomaly detection Ranking Video classification Automatic labeling via machine learning IP protection Automated billing and metering SELLERS Broad selection of paid, free, and open-source algorithms and models Data protection Discoverable on your AWS bill BUYERS

All rights reserved | Fully managed data processing jobs and data labeling workflows One-click collaborative notebooks and built- in, high performance algorithms and models One-click training Debugging and optimization One-click deployment and autoscaling Amazon SageMaker helps you build, train, and deploy models Visually track and compare experiments Automatically spot concept drift Fully managed with auto-scaling for 75% less Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Add human review of predictions Web-based IDE for machine learning Automatically build and train models

All rights reserved | Train your model with one click using Amazon SageMaker Not memory bound Checkpoint for re-training Train on a data stream Distributed by default Train with your own algorithms Single pass training

All rights reserved | Amazon SageMaker Automatic Model Tuning Automatically tune hyperparameters in your algorithms Neural Networks Number of layers Hidden layer width Learning rate Embedding dimensions Dropout Decision Trees Tree depth Max leaf nodes Gamma Eta Lambda Alpha Tuning at scale Adjust thousands of different combinations of algorithm parameters Automated Uses ML to find the best parameters Faster Eliminate days or weeks of tedious manual work Examples

All rights reserved | Organize, track, and compare training experiments Amazon SageMaker Experiments Tracking at scale Visualization Metrics and logging Fast Iteration Track parameters & metrics across experiments & users Custom organization Organize experiments by teams, goals, & hypotheses Easily visualize experiments and compare Log custom metrics using the Python SDK & APIs Quickly go back & forth & maintain high-quality

All rights reserved | Analysis and debugging, explainability, and alert generation Amazon SageMaker Debugger Data analysis & debugging Relevant data capture Automatic error detection Improved productivity with alerts Visual analysis and debugging Analyze & debug data with no code changes Data is automatically captured for analysis Errors are automatically detected based on rules Take corrective action based on alerts Visually analyze & debug from SageMaker Studio

All rights reserved | Fully managed data processing jobs and data labeling workflows One-click collaborative notebooks and built- in, high performance algorithms and models One-click training Debugging and optimization One-click deployment and autoscaling Amazon SageMaker helps you build, train, and deploy models Visually track and compare experiments Automatically spot concept drift Fully managed with auto-scaling for 75% less Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Collect and prepare training data Choose or build an ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Add human review of predictions Web-based IDE for machine learning Automatically build and train models

All rights reserved | Amazon SageMaker Model Monitor Continuous monitoring of models in production Automatic data collection Continuous Monitoring CloudWatch Integration Data is automatically collected from your endpoints Automate corrective actions based on Amazon CloudWatch alerts Visual data analysis Define a monitoring schedule and detect changes in quality against a pre-defined baseline See monitoring results, data statistics, and violation reports in SageMaker Studio Flexibility with rules Use built-in rules to detect data drift or write your own rules for custom analysis

All rights reserved | 47 © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved | 4 ML Learning Paths Decision Maker Developer Data Scientist Data Platform Engineer Exam Preparation https://aws.amazon.com/training/learning-paths/machine-learning/

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AI/ML on AWS By Ahmed Raafat

AI/ML on AWS By Ahmed Raafat

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