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DF1 - ML - Petukhov - Azure Ml Machine Learning as a Service

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Presentation from Moscow Data Fest #1, September 12.

Moscow Data Fest is a free one-day event that brings together Data Scientists for sessions on both theory and practice.

Link: http://www.meetup.com/Moscow-Data-Fest/

Published in: Science
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DF1 - ML - Petukhov - Azure Ml Machine Learning as a Service

  1. 1. Microsoft Azure ML: Machine Learning as a Service Dmitry Petukhov #MoscowDataFest
  2. 2. Storage Resource Management ML Framework Execution Engine Local OS Local Disc PythonRuntime YetAnother Runtime scikit learn HDFS YARN MapReduce Mahout HDFS / S3 YARN / Apache Mesos Spark MLlib HDFS / S3 YARN / Apache Mesos Python / R on Spark Python / R tools Spark Distributed FS Dark Magic… Local PC Hybrid Model Cluster (on-premises/cloud) ML as a Service (cloud) Challenge some library Python / R tools
  3. 3. Intro <- function() { Hello Data Fest! I need your help } Learn <- function() { Azure ML Overview # +Hello Azure ML Demo Data Science Workflow vs Azure ML } Code <- function() { ML Skills Cluster Analysis # Demo 1 Twitter sentiment analysis # Demo 2 } Coffee <- function() { Q&A Contacts } Agenda
  4. 4. Dmitry Petukhov, Software Architect + Developer, Microsoft Certified Professional (C#), Big Data Enthusiast && Coffee Addict Researcher & Developer @ OpenWay Hello Data Fest! Azure Machine Learning. Introduction
  5. 5. Guiding Principles Reduce complexity to broaden participation No software to install, only web browser; Possibility to develop without writing line of code; Easy deployment and usage using restfull API; Easy collaboration on Azure ML projects; Visual composition with end2end support for Data Science workflow; Extensible, support for R OSS. Data Science is far too complex today Math Computer Science Domain Reference: TechEd 2014 Conference Azure Machine Learning. Overview
  6. 6. Data Azure Machine Learning Consumers Local storage Upload data from PC… Cloud storage Azure Storage Azure Table Hive etc. Excel Business Apps Reference: TechEd 2014 Conference Azure Machine Learning. Overview Business problem Modeling Business valueDeployment Azure Marketplace (Applications store) Azure ML Gallery (community) ML Web Services (REST API Services) ML Studio (Web IDE) Workspace: Experiments Datasets Trained models Notebooks Access settings Data Model API Manage API
  7. 7. Step 3. Create Azure ML Workspace Step 4. Go to Azure ML Studio & create ML Experiment Step 5. Publish result Azure Machine Learning. Overview Demo #0: Hello Azure ML! Step 1. Get $200 credit Sign up for Azure free trial. Step 2. Get access to Azure Portal
  8. 8. Azure Machine Learning. Azure ML Flow Supervised Learning Flow Part #1
  9. 9. Azure Machine Learning. Azure ML Flow Supervised Learning Flow Part #2 Source
  10. 10. Azure Machine Learning. Azure ML Flow Source: Azure ML Cheat Sheet
  11. 11. Demo #1: ML Skills Cluster Analysis Azure Machine Learning. Demo k-means clustering aims to partition the n observations into k (≤ n) sets S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of squares (WCSS). where (x1, x2, …, xn) – observations, μi is the mean of points in Si. Source: Wikipedia
  12. 12. Demo #2: Twitter sentiment analysis Azure Machine Learning. Demo TD-IDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. Source: Wikipedia
  13. 13. Restrictions Legislative restrictions International & local Azure platform restrictions Max storage volume per account, etc. Azure ML service restrictions Data Max dataset volume: 10 Gb Vector size limitation: 2^64 Throttled policy 20 concurrent request per endpoint Max endpoints count: 10K Black box No debug No Scala, C++, C# No your own right algorithms Azure Machine Learning. Conclusion
  14. 14. Killer Features R (quickstart) Support R models & scripts Python (quickstart) Support Python scripts Jupyter Notebooks in Azure ML Studio Publishing REST API & real-time mode vs batch-mode Azure ML Gallery Share for community Azure Marketplace SaaS store In-the-box integration with… Hive, Azure Storage, Excel, Cortana Analytics Stack Free Start & it’s child age Azure Machine Learning. Conclusion
  15. 15. Nothing has changed Reduce complexity to broaden participation No software to install, only web browser; Possibility to develop without writing line of code; Easy deployment and usage using restfull API; Easy collaboration on Azure ML projects; Visual composition with end2end support for Data Science workflow; Extensible, support for R OSS. Data Science still too complex today Math Computer Science Domain Azure Machine Learning. Conclusion
  16. 16. References Start from azure.com/ml Microsoft Machine Learning Blog Azure ML documentation + free online course, videos & books Microsoft Research: Azure for Researchers Azure Machine Learning. Conclusion
  17. 17. © 2015 Dmitry Petukhov All rights reserved. Microsoft Azure and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. Thank you!
  18. 18. Q&A Now or later (send on d.petukhov@outlook.com) Stay connected Facebook: @code.zombi LinkedIn: @dpetukhov Habr: @codezombie All contacts… Read my tech code instinct blog Download presentation from http://0xcode.in/moscow-data-fest or Azure ML: Machine Learning as a Service

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