Machine Learning as a Service with Amazon Machine Learning
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- Amazon Machine Learning is a managed machine learning service that allows developers to build, train, and deploy machine learning models without having to manage any infrastructure. - It provides powerful machine learning technology based on Amazon's internal systems and integrates with AWS data services like S3, Redshift, and RDS. Models can be deployed for batch or real-time predictions. - Fraud.net and Upserve are examples of customers using Amazon Machine Learning to build over 20 fraud detection models and over 100 predictive analytics models for restaurants to detect fraud and predict business patterns.
Machine Learning as a Service with Amazon Machine Learning
- 1. Machine Learning as a Service with Amazon Machine Learning Julien Simon, AI Evangelist, EMEA @julsimon
- 2. What to expect • Amazon Machine Learning • Use cases and architecture patterns • Building models + demo • Q&A
- 4. Amazon ML Easy to use, managed machine learning service built for developers Robust, powerful machine learning technology based on Amazon’s internal systems Create models using your data already stored in the AWS cloud Deploy models to production in seconds
- 5. Easy to use and developer-friendly Use the intuitive service console to build and explore your initial models • Data retrieval • Model training, quality evaluation, fine-tuning • Deployment and management Automate model lifecycle with fully featured APIs • Java, Python, .NET, JavaScript, Ruby, PHP Easily create smart iOS and Android applications with AWS Mobile SDK
- 6. Powerful machine learning technology Based on Amazon’s battle-hardened internal systems Not just the algorithms: • Smart data transformations • Input data and model quality alerts • Built-in industry best practices Grows with your needs • Train on up to 100 GB of data • Generate billions of predictions • Obtain predictions in batches or real-time
- 7. Integrated with AWS data ecosystem Access data that is stored in S3, Amazon Redshift, or MySQL databases in RDS Output predictions to S3 for easy integration with your data flows Use AWS Identity and Access Management (IAM) for fine-grained data-access permission policies
- 8. Fully managed prediction services End-to-end service, with no servers to provision and manage One-click production model deployment Programmatically query model metadata to enable automatic retraining workflows Monitor prediction usage patterns with Amazon CloudWatch metrics
- 9. Pay-as-you-go and inexpensive Data analysis, model training, and evaluation: $0.42/instance hour Batch predictions: $0.10/1000 Real-time predictions: $0.10/1000 + hourly capacity reservation charge
- 10. ” “ Fraud.net Uses AWS to Quickly, Easily Detect Online Fraud Fraud.net is the world’s leading crowdsourced fraud prevention platform. Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns. • Needed to build and train a larger number of more targeted machine-learning models • Uses Amazon Machine Learning to provide more than 20 models • Easily builds and trains models to effectively detect online payment fraud • Reduces complexity and makes sense of emerging fraud patterns • Saves clients $1 million weekly by helping them detect and prevent fraud Oliver Clark CTO, Fraud.net ” “
- 11. ” “ Upserve Uses AWS to Help Restaurants Predict Business Upserve provides online payment and analytical software to thousands of restaurant owners throughout the U.S. Using Amazon Machine Learning, we can predict the total number of customers who will walk through a restaurant’s doors in a night. • Needed its restaurant management platform to provide more predictive analytics • Builds and trains more than 100 machine learning models weekly • Streams restaurant sales and menu item data in real time • Helps restaurateurs predict nightly business Bright Fulton Director of Infrastructure Engineering, ” “
- 12. Building models
- 13. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building models with Amazon ML
- 14. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building models with Amazon ML - Create a Datasource object pointing to your data - Explore and understand your data - Transform data and train your model
- 15. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building models with Amazon ML - Understand model quality - Adjust model interpretation
- 16. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building models with Amazon ML - Batch predictions - Real-time predictions
- 19. Data Visualization & Analysis Business Problem ML problem framing Data Collection Data Integration Data Preparation & Cleaning Feature Engineering Model Training & Parameter Tuning Model Evaluation Are Business Goals met? Model Deployment Monitoring & Debugging – Predictions YesNo DataAugmentation Feature Augmentation Re-training Scope of Amazon ML
- 20. Batch predictions with EMR Query for predictions with Amazon ML batch API Process data with EMR Raw data in S3 Aggregated data in S3 Predictions in S3 Your application
- 21. Batch predictions with Amazon Redshift Structured data In Amazon Redshift Load predictions into Amazon Redshift Predictions in S3 Query for predictions with Amazon ML batch API Your application -or- Read prediction results directly from S3
- 22. Real-time predictions for interactive applications Your application Query for predictions with Amazon ML real-time API
- 23. Adding predictions to an existing data flow Your application Amazon DynamoDB + Trigger event with Lambda + Query for predictions with Amazon ML real-time API
- 24. Predicsis.ai End-to-end Machine Learning automation
- 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/aml/ https://aws.amazon.com/blogs/ai https://predicsis.ai/ https://medium.com/@julsimon
- 26. Thank you! Julien Simon, AI Evangelist, EMEA @julsimon