Voxxed Days Thesaloniki 2016 - Machine Learning for Developers
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This document discusses machine learning and how it can be used by developers. It covers topics like supervised learning, unsupervised learning, reinforcement learning, and different machine learning algorithms. It also discusses tools for machine learning like Amazon EMR, Spark, Amazon Machine Learning service, and deep learning with DSSTNE. Finally, it provides an example of how to build a smart mobile app using serverless AWS services like Lambda, Kinesis, S3, Cognito and others with machine learning models.
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Voxxed Days Thesaloniki 2016 - Machine Learning for Developers
- 1. Machine Learning for Developers Danilo Poccia, Technical Evangelist @danilop danilop
- 2. Credit: Gerry Cranham/Fox Photos/Getty Images http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/
- 3. Credit: Gerry Cranham/Fox Photos/Getty Images http://www.telegraph.co.uk/travel/destinations/europe/united-kingdom/england/london/galleries/The-history-of-the-Tube-in-pictures-150-years-of-London-Underground/1939-ticket-examin/ 1939 London Underground
- 4. Batch Report
- 7. Predictions
- 10. Model
- 11. Machine Learning
- 12. Supervised Learning Machine Learning Unsupervised Learning The task of inferring a model from labeled training data The task of inferring a model to describe hidden structure from unlabeled data
- 13. Reinforcement Learning Perform a certain goal in a dynamic environment, without an explicit “teacher”
- 14. Driving a vehicle Playing a game against an opponent R einforcem ent Learning
- 20. Training from Labeled Data Supervised Learning Training Validation 70% 30%
- 21. Be Careful of Overfitting Supervised Learning
- 22. Be Careful of Overfitting Supervised Learning
- 23. Be Careful of Overfitting Supervised Learning
- 26. ?Data Model
- 27. Amazon EMR with Spark (MLib) Data Model
- 29. Recommender: An Analysis of Collaborative Filtering Techniques Christopher R. Aberger http://stanford.io/28OR3XE M ore Info
- 30. Amazon EMR with Spark (MLib) Data Model
- 32. Amazon Machine Learning (Amazon ML) Data Model
- 33. Amazon Machine Learning (Amazon ML) Data Model Batch Predictions
- 34. Amazon Machine Learning (Amazon ML) Data Model Batch Predictions Real-time Predictions
- 35. Binary Classification Multiclass Classification Regression Logistic Regression (Logistic Loss Function + SGD) Multinomial Logistic Regression (Multinomial Logistic Loss + SGD) Linear Regression (Squared Loss Function + SGD) The optimization technique used in Amazon ML is online Stochastic Gradient Descent (SGD)
- 37. Amazon Machine Learning (Amazon ML) Data Model Batch Predictions Real-time Predictions
- 38. What about Deep Learning?
- 44. Deep Scalable Sparse Tensor Network Engine (DSSTNE) Pronounced “Destiny” An Amazon developed library for building Deep Learning (DL) Machine Learning (ML) models https://github.com/amznlabs/amazon-dsstne O pen Source
- 45. DSSTNE features for production workloads Multi-GPU Scale Training and prediction both scale out to use multiple GPUs, spreading out computation and storage in a model-parallel fashion for each layer Large Layers Model-parallel scaling enables larger networks than are possible with a single GPU Sparse Data DSSTNE is optimized for fast performance on sparse datasets. Custom GPU kernels perform sparse computation on the GPU, without filling in lots of zeroes
- 47. Amazon EC2 P2 Instances Up to: • 16 NVIDIA K80 GPUs • 64 vCPUs 732 GiB of host memory • combined 192 GB of GPU memory • 40 thousand parallel processing cores • 70 teraflops (single precision) • over 23 teraflops (double precision). • GPUDirect™ for up to 16 GPUs
- 48. DSSTNEData Model
- 49. Let’s Build a “Smart” Mobile App
- 50. Real-time Predictions AWS Lambda Function(s) Amazon Machine Learning Model Amazon Kinesis Stream Amazon Redshift Database Amazon S3 Bucket Amazon Cognito Identity Amazon SNS Mobile Push Amazon Mobile Analytics Amazon Kinesis Firehose “Smart” Mobile App
- 51. AWS Lambda Function(s) Amazon Machine Learning Model Amazon Kinesis Stream Amazon Redshift Database Amazon S3 Bucket Amazon Cognito Identity Amazon SNS Mobile Push Amazon Mobile Analytics Amazon Kinesis Firehose “Smart” Mobile App Where are the Servers?
- 52. Where are the Servers? Build Event-Driven Serverless Apps And Focus on Your Idea
- 54. Build Apps With Services, Not Servers