1. 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

2. 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

3. 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

4. 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 & OpenWay
Hello Data Fest!

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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.

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

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.
Demo #2: Twitter sentiment analysis
Source: Wikipedia

13. 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

14. 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

15. 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

16. 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

17. 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.

18. 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
Stay connected
All contacts…
Read my tech code instinct blog
Download presentation from or