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Voxxed Days Thesaloniki 2016 - Machine Learning for Developers
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 DaniloPoccia, Technical Evangelist @danilop danilop
- 2. Credit: Gerry Cranham/FoxPhotos/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/FoxPhotos/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
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- 12. Supervised Learning Machine Learning Unsupervised Learning The taskof 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 goalin a dynamic environment, without an explicit âteacherâ
- 14. Driving a vehicle Playinga game⨠against an opponent R einforcem ent Learning
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- 20. Training from LabeledData Supervised⨠Learning Training Validation 70% 30%
- 21. Be Careful ofOverďŹtting Supervised⨠Learning
- 22. Be Careful ofOverďŹtting Supervised⨠Learning
- 23. Be Careful ofOverďŹtting Supervised⨠Learning
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- 27. Amazon EMR with Spark(MLib) Data Model
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- 29. Recommender: An Analysisof Collaborative Filtering Techniques Christopher R. Aberger http://stanford.io/28OR3XE M ore Info
- 30. Amazon EMR with Spark(MLib) Data Model
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- 33. Amazon⨠Machine Learning⨠(Amazon ML) DataModel Batch Predictions
- 34. Amazon⨠Machine Learning⨠(Amazon ML) DataModel Batch Predictions Real-time Predictions
- 35. Binary ClassiďŹcation Multiclass ClassiďŹcation Regression Logistic Regression⨠(LogisticLoss 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)
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- 37. Amazon⨠Machine Learning⨠(Amazon ML) DataModel Batch Predictions Real-time Predictions
- 38. What about DeepLearning?
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- 44. Deep Scalable Sparse TensorNetwork Engine (DSSTNE) Pronounced âDestinyâ 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 forproduction 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 ďŹlling in lots of zeroes
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- 47. Amazon EC2 P2Instances 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 teraďŹops (single precision) ⢠over 23 teraďŹops (double precision). ⢠GPUDirect⢠for up to 16 GPUs
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- 49. Letâs Build aâSmartâ 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 AmazonSNS Mobile Push Amazon Mobile Analytics Amazon Kinesis Firehose âSmartâ Mobile App
- 51. AWS Lambda Function(s) Amazon Machine Learning Model Amazon Kinesis Stream Amazon Redshift Database Amazon S3 Bucket Amazon Cognito Identity AmazonSNS Mobile Push Amazon Mobile Analytics Amazon Kinesis Firehose âSmartâ Mobile App Where are the Servers?
- 52. Where are the Servers? BuildEvent-Driven Serverless Apps And Focus on Your Idea
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- 54. Build Apps WithServices, Not Servers
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