Machine Learning as a Service 𝑃 𝐴 𝑗 𝐵 = 𝑃 𝐴 𝑗 𝑃(𝐵| 𝐴 𝑗 ) 𝑖=1 𝑁 𝑃 𝐴 𝑖 𝑃(𝐵| 𝐴 𝑖 ) Microsoft Azure ML: Machine Learning as a Service Dmitry Petukhov Ω,𝔘, ℙ #MoscowDataFest

Cluster (on-premises/cloud) Challenge ML as a Service (cloud) Local PC Hybrid Model Cluster (on-premises/cloud) ML Framework scikit learn some library Python / R tools MLlib Mahout Python / R tools Python Runtime Yet Another Runtime Python / R on Spark Dark Magic… Execution Engine Spark MapReduce Spark Resource Management YARN / Apache Mesos YARN / Apache Mesos YARN Local OS Storage Local Disc HDFS / S3 HDFS / S3 HDFS Distributed FS

Agenda 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

Hello Data Fest! Dmitry Petukhov, Software Architect + Developer, Azure Machine Learning. Introduction Dmitry Petukhov, Software Architect + Developer, Microsoft Certified Professional (C#), Big Data Enthusiast && Coffee Addict Researcher & Developer @ OpenWay Hello Data Fest!

Guiding Principles Data Science is far too complex today Azure Machine Learning. Overview Data Science is far too complex today Math Computer Science Domain 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. Guiding Principles Reference: TechEd 2014 Conference

Azure Machine Learning Azure Machine Learning. Overview Data Azure Machine Learning Consumers Data Model API ML Studio (Web IDE) ML Web Services (REST API Services) Cloud storage Azure Storage Azure Table Hive etc. Excel Manage Workspace: Experiments Datasets Trained models Notebooks Access settings API Azure Marketplace (Applications store) Local storage Upload data from PC… Business Apps Azure ML Gallery (community) Business problem Modeling Deployment Business value Reference: TechEd 2014 Conference

Demo #0: Hello Azure ML! Step 1. Get $200 credit Step 2. Get access to Azure Machine Learning. Overview Step 1. Get $200 credit Sign up for Azure free trial. Step 2. Get access to Azure Portal Demo #0: Hello Azure ML! Step 3. Create Azure ML Workspace Step 4. Go to Azure ML Studio & create ML Experiment Step 5. Publish result

Supervised Learning Flow Part #1 Azure Machine Learning. Azure ML Flow Supervised Learning Flow Part #1 Feature extraction - process attempts to create additional relevant features from the existing raw features in the data, and to increase predictive power to the learning algorithm

Supervised Learning Flow Part #2 Azure Machine Learning. Azure ML Flow Supervised Learning Flow Part #2 Feature selection often increases classification accuracy by eliminating irrelevant, redundant, or highly correlated features. Second, it decreases the number of features which makes model training process more efficient – compute and algorithm performance. This is particularly important for learners that are expensive to train such as support vector machines. Process selects the key subset of original data features in an attempt to reduce the dimensionality of the training problem. PCA & ICA LDA, Fisher root mean square error (RMSE) Source

Azure Machine Learning. Azure ML Flow Source: Azure ML Cheat Sheet Accuracy = (TP+TN)/(P + N): ratio of correctly predicted observations. Using accuracy is only good for symmetric data sets where the class distribution is 50/50. Fraction of correct predicted data Precision = TP/(TP+FP): looks at the ratio of correct positive observations. Recall = TP/(TP+FN): ratio of correctly predicted positive events.

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. Demo #1: ML Skills Cluster Analysis Source: Wikipedia

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. Demo #2: Twitter sentiment analysis Source: Wikipedia

Restrictions Legislative restrictions Azure platform restrictions Azure Machine Learning. Conclusion 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 Restrictions

Killer Features R (quickstart) Python (quickstart) Publishing Azure Machine Learning. Conclusion 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 Killer Features

Data Science still too complex today Azure Machine Learning. Conclusion Data Science still too complex today Math Computer Science Domain 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. Nothing has changed

References Start from azure.com/ml Microsoft Machine Learning Blog Azure Machine Learning. Conclusion Start from azure.com/ml Microsoft Machine Learning Blog Azure ML documentation + free online course, videos & books Microsoft Research: Azure for Researchers References

Thank you! © 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.

Now or later (send on d.petukhov@outlook.com) Stay connected Azure ML: Machine Learning as a Service 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