Predictive Analytics with Amazon Sagemaker

Predictive Analytics with Amazon SageMaker

Steve Shirkey Specialist SA, AWS (Singapore)

Image recognition and tagging for photo organization

Object detection, tracking and navigation for Autonomous Vehicles

Speech recognition & synthesis in Intelligent Voice Assistants

Algorithmic trading strategy performance improvement

Sentiment analysis for targeted advertisements

Fulfillment & Search & Existing New At logistics discovery products products AWS

© 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.

• Applied research • Q&A systems • Core research • Supply chain optimization • Alexa • Advertising • Demand forecasting • Machine translation • Risk analytics • Video content analysis • Search • Robotics • Recommendations • Lots of computer vision… • AI services • NLP/NLU

APPLICATION SERVICES

Rekognition Transcribe Translate Polly Comprehend Lex

PLATFORM SERVICES

Amazon SageMaker Amazon EMR (Spark ML) Amazon Mechanical Turk AWS DeepLens

FRAMEWORKS & INTERFACES AWS Deep Learning AMIs Apache Caffe2 CNTK PyTorch TensorFlow Chainer Keras Gluon MXNet

Re-training ML problem framing Data Collection

Data Integration

Monitoring & Data Preparation & Feature Engineering Debugging Cleaning

Data Augmentation Data Model Training & Parameter Tuning Data Visualization & – Predictions Analysis

Model Evaluation Model Deployment

No Yes Augmentation Feature Are Business Goals met?

Re-training ML problem framing Data Collection

Data Integration

Monitoring & • Help formulate the right Data Preparation & Feature Engineering Debugging questions Cleaning • Domain Knowledge

Data Augmentation Data Model Training & Parameter Tuning Data Visualization & – Predictions Analysis

Model Evaluation Model Deployment

No Yes Augmentation Feature Are Business Goals met?

Retraining ML problem framing Data Collection

Data Integration

Monitoring & • Build the data platform: Data Preparation & Feature Engineering Debugging • Amazon S3 Cleaning • AWS Glue • Amazon Athena

Data Augmentation Data Model Training & • Amazon EMR Parameter Tuning – Predictions • Amazon Redshift Data Visualization & Spectrum Analysis

Model Evaluation Model Deployment

No Yes Augmentation Feature Are Business Goals met?

• Setup and manage Notebook Environments

• Setup and manage Feature Engineering Training Clusters

• Write Data Connectors Model Training & Parameter Tuning Data Visualization & • Scale ML algorithms to Analysis large datasets Model Evaluation

• Distribute ML training algorithm to multiple machines

• Secure Model artifacts

• Setup and manage Model Inference Clusters

Monitoring & • Manage and Scale Model Debugging Inference APIs

• Monitor and Debug Model – Predictions Predictions

• Models versioning and Model Deployment performance tracking

• Automate New Model version promotion to production (A/B testing)

Easily build, train, and deploy machine learning models

Collect and Choose and Set up and Train and tune Deploy model Scale and prepare training optimize your manage model in production manage the data ML algorithm environments for (trial and error) production training environment

Easily build, train, and deploy machine learning models

Pre-built Built-in, high notebooks performance Set up and Train and Scale and Deploy model for common algorithms manage tune model manage the problems environments (trial and in production production for training error) environment

BUILD

SageMaker Built-in Algorithms k-Means Clustering PCA Neural Topic Modelling Factorisation Machines Linear Learner XGBoost Latent Dirichlet Allocation Image Classification Seq2Seq DeepAR Forecasting BlazingText (word2vec) Random Cut Forest k-Nearest Neighbor Object Detection

SageMaker Built-in Bring Your Own Algorithms Algorithms

K-means Clustering ML Algorithms PCA R Neural Topic Modelling MXNet Factorisation Machines TensorFlow Linear Learner – Regression Caffe XGBoost PyTorch Latent Dirichlet Allocation Keras Image Classification CNTK Seq2Seq … Linear Learner – Classification DeepAR Forecasting

SageMaker Built-in Bring Your SageMaker Algorithms Own Framework SDKs Algorithms K-means Clustering ML Algorithms TensorFlow SDK PCA R MXNet (Gluon) SDK Neural Topic Modelling MXNet Chainer SDK Factorisation Machines TensorFlow PyTorch SDK Linear Learner – Regression Caffe XGBoost PyTorch Latent Dirichlet Allocation Keras Image Classification CNTK Seq2Seq … Linear Learner – Classification DeepAR Forecasting

SageMaker Built-in Bring Your SageMaker Apache Spark Algorithms Own Framework SDK Estimator Algorithms TensorFlow SDK Apache Spark Python library K-means Clustering ML Algorithms MXNet (Gluon) SDK Apache Spark Scala library PCA R Chainer SDK Neural Topic Modelling MXNet PyTorch SDK Factorisation Machines TensorFlow Linear Learner – Regression Caffe XGBoost PyTorch Latent Dirichlet Allocation Keras Image Classification CNTK Amazon EMR Seq2Seq … Linear Learner – Classification DeepAR Forecasting

Easily build, train, and deploy machine learning models

Pre-built Built-in, high One-click Hyperparameter training tuning notebooks performance Scale and for common algorithms Deploy model manage the problems in production production environment

BUILD TRAIN

Easily build, train, and deploy machine learning models

Pre-built Built-in, high One-click Hyperparameter One-click Fully managed notebooks performance training tuning deployment hosting with for common algorithms auto-scaling problems

BUILD TRAIN DEPLOY

SageMaker Training Notebooks Algorithm

Algorithm Raw Prepared Container User HPO Data Data Interactions

Training Data Inference requests

Amazon S3 SageMaker SageMaker Training Hosting AWS API Trained Lambda Gateway Model Trained Model

Amazon SageMaker Amazon ECR

Training code

Model Training (on EC2)

Amazon SageMaker Amazon ECR Trainingdata Training code Training code Helper code

Model Training (on EC2)

Amazon SageMaker Amazon ECR

Inference code

Modelartifacts Trainingdata Training code Training code Helper code

Model Training (on EC2)

Amazon SageMaker Amazon ECR

Inference code Helper code Inference code

Model Hosting (on EC2)

Modelartifacts Trainingdata Training code Training code Helper code

Model Training (on EC2)

Inference response Inference request Amazon SageMaker Amazon ECR

Inference Endpoint

Inference code Helper code Inference code

Model Hosting (on EC2)

Modelartifacts Trainingdata Training code Training code Helper code

Model Training (on EC2)

Inference response Inference request Amazon SageMaker Amazon ECR

Inference Endpoint Ground Ground Truth

Inference code Helper code Inference code

Model Hosting (on EC2)

Modelartifacts Trainingdata Training code Training code Helper code

Model Training (on EC2)

© 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved. Photo credit: https://www.flickr.com/photos/ceasedesist/5821282085 (Licensed for commercial use) © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018 Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Option 1: Grid Search Regularization

Learning Rate

“Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as

Regularization good or better within a small fraction of the computation time.” Learning Rate

Bergstra, Bengio, “Random Search for Hyper-Parameter Optimization” https://dl.acm.org/citation.cfm?id=2188395

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (https://arxiv.org/abs/1012.2599)

• Practical Bayesian Optimization of Machine Learning Algorithms (https://arxiv.org/abs/1206.2944)

• Taking the Human Out of the Loop: A Review of Bayesian Optimization (https://ieeexplore.ieee.org/document/7352306)

Amazon SageMaker’s Hyperparameter Tuning feature is based on an implementation of Bayesian Optimization, along with some additional optimizations

• Getting started with Amazon SageMaker: • https://aws.amazon.com/sagemaker/

• Use the Amazon SageMaker SDK: • For Python: https://github.com/aws/sagemaker-python-sdk • For Spark: https://github.com/aws/sagemaker-spark

• SageMaker Code Samples / Workshops: • https://github.com/awslabs/amazon-sagemaker-examples • https://github.com/awslabs/amazon-sagemaker-workshop

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 Brainstorming Modeling Teaching

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• Enterprise solutions • Big Data solutions architects architects

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