#azure

[Video] AI, Deep Learning and Machine Learning

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I watched this video over the weekend and wanted to share this very well done presentation by a Venture Capital (VC) firm with you — that’s why I love following VC’s (especially one’s who invest in Data/Analytics theme) since they tend to share some amazing insights on where the industry is going.

Abstract:
“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.

Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple “A.I. winters”? What’s the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?

From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.

URL: http://a16z.com/2016/06/10/ai-deep-learning-machines/

Why you should use “Tune Model Hyper parameters” module in Azure Machine Learning?

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A machinAuzre Machine Learninge learning algorithm in Azure ML has few parameter settings that you can set — in this post, we will talk about 1) why you should NOT stick with default settings 2) How can “Tune model hyperparameters” module help you do so?

So first up, why you should not be using the default setting? The parameter settings that are applied to a model impacts the accuracy (or call it predictive power) of the algorithm…sometimes it may be significant and sometimes not but either case, you won’t know until you try changing the default values. In other words, by tuning the hyperparameters you could significantly boost your model’s performance!

Now, how do you go about setting the parameters such that it gives optimal performance? Let’s say that there are 3 parameters then that is 27 different combinations! How do you know which one is the best? You could dig a little deeper into the mechanism of how algorithms works and narrow down your list but that would still take some time. So, there should be a better way, right? Luckily there is: This where “Tune Model Hyperparameters” comes in! You can use it with any algorithm in Azure ML. This module helps you tune the hyper-parameters. There’s some things that you still need do like decide: Do you want the module to just try random n combinations? OR Do you want the module to try all combinations (fyi: this is compute-intensive operation!)? … AND you will have to decide what are you are optimizing for? Depending on the algorithm it would let you pick the evaluation metric that you want to optimize.

Now, there are some good articles already written that walks you through how to get about doing this so I am going to share these links with you:

  1. Tune Hyper Parameters (MSDN)
  2. Understanding sweep parameters module in Azure ML

I want to conclude by sharing few tips:

 Notes from the field:

  1. Running the “Entire Grid” mode will slow down the training time for the model. You might want to make sure that it’s acceptable and the cost (longer training time) to benefit (better accuracy) is worth it for your case
  2. When you are comparing algorithms to decide the best one that fits your problem, instead of comparing “model with default parameter settings” with each other, try comparing the “model with tuned hyper-parameters”

Amazon ML vs Microsoft Azure ML vs Databricks Cloud — my guest post comparing cloud Machine Learning platforms on BigDataCloud.com

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I wrote a post on BigDataCloud.com community comparing cloud based Machine Learning platforms: Amazon ML vs Microsoft Azure ML vs Databricks Cloud 

My goal for the post were to: 1) share a framework to compare cloud-based Machine Learning platforms 2) Apply the framework to three platforms to see how they stack up.

Here’s the framework:

Auzre ML vs Amazon ML

Please read the rest of the post on BigDataCloud.com. Azure ML vs Amazon ML vs Databricks Cloud.

Webinar: Learn how to build a Machine Learning model to predict customer churn

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UPDATE: WEBINAR HAS ENDED. RECORDING CAN BE FOUND HERE:

https://www.youtube.com/user/PASSBAVC

Next week, on Mar 15th, 2016, We at Business Analytics VC are hosting a webinar to help you dive a little bit deeper with azure machine learning and learn about building a model to predict customer churn. Even if you don’t use Azure, I think you can still benefit from learning about the use-cases and the framework to solve this problem. You can register using this URL:

http://bit.ly/PASSBAVC031516

see you there!

PS: if you like to learn about how to build a recommend-er system in Azure ML then you can see last month’s presentation here: http://bavc.sqlpass.org/Home.aspx?EventID=4514

Machine Learning Algorithm Cheat Sheet:

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If you’re getting started with Data Science & Machine Learning then I think this would be a great resource for you. This “cheat sheet” helps you select the “algorithm” to test depending on the problem you are trying to solve and the data-set that you have.

Download link: http://aka.ms/MLCheatSheet (Courtesy: Azure Machine Learning)

Also, even though the cheat sheet was created to help you with “Azure Machine learning” product, it’s still valid if you use other machine learning tools.

Azure Machine Learning Algorithm Cheat Sheet

 

News from PASS Summit’14 for Business Analytics Professionals: #sqlpass #summit14

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This post is a quick summary for all Business Analytics related updates that I saw at PASS Summit’14:

1. Theme of the Keynote(s)/Session(s) seemed to be around educating the community about the benefits of the NEW(er) tools. I saw demos/material for cloud-based tools like SQL databases, Azure stream analytics, Azure DocumentDB, AzureHDInsight & Azure Machine learning. The core message was pretty clear: A data professional does two things – 1) Guards data OR 2) helps to generate Insights from Data – And they will need to keep up-to-date on the new tools to future-proof their career.

Read more about this here: http://blogs.technet.com/b/dataplatforminsider/archive/2014/11/05/microsoft-announces-major-update-to-azure-sql-database-adds-free-tier-to-azure-machine-learning.aspx

2. Coming soon: Power BI will be able to connect to on-premise SSAS data sources (multi-dim & tabular).

3. Coming soon: A better experience to create Power BI dashboards.

Read more about Power BI updates here: http://www.jenunderwood.com/2014/11/05/pass-summit-2014-bi-news/

4. Azure Machine Learning adds a free-tier! You won’t need a credit-card/subscription to sign up for this.

5. I also saw sessions proposing new way of thinking about an architecture for “Self Service BI” and “Big Data” which might be worth following because since these are newer tools, it’s definitely worth considering an architecture that’s designed to make the most of the investments in these new tools. That’s it & I’ll leave you with a quote from James Phillips from Day 1’s keynote:

PASS Business Analytics VC: Insider’s Introduction to Microsoft Azure Machine Learning (#AzureML). #sqlpass

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Insider’s Introduction to Microsoft Azure Machine Learning (AzureML)

Thu, Sep 18, 2014 12:00 PM – 1:00 PM EDT

RSVP: http://bit.ly/PASSBAVC091814

Session Abstract:
Microsoft has introduced a new technology for developing analytics applications in the cloud. The presenter has an insider’s perspective, having actively provided feedback to the Microsoft team which has been developing this technology over the past 2 years. This session will 1) provide an introduction to the Azure technology including licensing, 2) provide demos of using R version 3 with AzureML, and 3) provide best practices for developing applications with Azure Machine Learning.
Speaker BIO:
Mark is a consultant who provides enterprise data science analytics advice and solutions. He uses Microsoft Azure Machine Learning, Microsoft SQL Server Data Mining, SAS, SPSS, R, and Hadoop (among other tools). He works with Microsoft Business Intelligence (SSAS, SSIS, SSRS, SharePoint, Power BI, .NET). He is a SQL Server MVP and has a research doctorate (PhD) from Georgia Tech.

RSVP: http://bit.ly/PASSBAVC091814

Hope to see you there!

Paras Doshi
Business Analytics Virtual Chapter’s Co-Leader

 

Back to basics: Multi Class Classification vs Two class classification.

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Classification algorithms are commonly used to build predictive models. Here’s what they do (simplified!):

Machine Learning Predictive Algorithms analytics Introduction

Now, here’s the difference between Multi Class and Two Class:

if your Test Data needs to be classified into two classes then you use a two-class classification model.

Examples:

1. Is it going to Rain today? YES or NO

2. Will the buyer renew his soon-to-expire subscription? YES or NO

3. What is the sentiment of this text? Positive OR Negative

As you can see from above examples the test data needs to be classified in two classes.

Now, look at example #3 – What is the sentiment of the text? What if you also want an additional class called “neutral” – so now there are three classes and we’ll need to use a multi-class classification model. So, If your test data needs to be classified into more than two classes then you use a multi-class classification model.

Examples:

1. Sentiment analysis of customer reviews? Positive, Negative, Neutral

2. What is the weather prediction for today? Sunny, Cloudy, Rainy, Snow

I hope the examples helped, so next time you have to choose between multi class and two class classification models, ask yourself – does the problem ask you to predict two classes or more? based on that, you’ll need to pick your model.

Example: Azure Machine Learning (AzureML) studio’s classifier list:

Azure Machine Learning classifiers list

I hope this helps!

Join #SQLPASS virtual chapters for free online #SQLServer learning!

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SQLPASS virtual chapters (VC) provides free sqlserver training year-round:

If you are not signed up already, then consider signing up! With that, And here’s a Quick walk-through on how to Join a VC:

If you do not have a SQLPASS account:

a. Go to http://www.sqlpass.org/RegisterforSQLPASS.aspx

b. Fill up the required information and register

Now, After successful login/registration:

a. Go to http://www.sqlpass.org/MYPASS.aspx

b. switch to MyChapters section

c. Now under virtual chapters, you would see a list of virtual chapters. Join the one’s you are interested in!

my PASS my Chapter Azure VC

And just getting the word out about the Azure VC’s next meeting on Dec 13th 2012 – 12 noon EST, Azure VC has a meeting, Details here:  Azure PASS VC Next meeting: Kung Fu Migration to Windows Azure SQL Database – I hope to see you there!

Azure PASS VC Next meeting: Kung Fu Migration to Windows Azure SQL Database

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Azure PASS VC’s next meeting:

Kung Fu Migration to Windows Azure SQL Database

Speaker: Scott Klein, Technical Evangelist Microsoft

Summary: As cloud computing becomes more popular and cloud-based solutions the norm rather than the fringe, the need to efficiently migrate your database is crucial. This demo-filled session will discuss the tips and tricks, methods and strategies for migrating your on-premises SQL Server databases to Windows Azure SQL Database, AKA SQL Azure. Focusing primarily on SQL Server Data Tools and the DAC Framework, this session will focus on how these tools can make you a kung-fu migration master.

About Scott: Scott Klein is a Corporate Technical Evangelist for Microsoft focusing on Windows Azure SQL Database (AKA SQL Azure) and related cloud-ready data services. His entire career has been built around SQL Server, working with SQL Server since the 4.2 days. Prior to Microsoft he was a SQL Server MVP for several years, then followed that up by being one of the first 4 SQL Azure MVPs. Scott is the author of over ½ dozen books for both WROX and APress, including Pro SQL Azure. He can be found talking about Windows Azure SQL Database and database scalability and performance at events large and small wherever he can get people to listen, such as SQL Saturday events, local SQL Server user groups, and TechEd.

Details at http://azure.sqlpass.org/

Download the calendar file: http://www.sqlpass.org/iCal.aspx?EventID=245

How to Join Azure PASS VC’s?

If you want to stay updated on meeting announcements, please consider registering on PASS’s website and Joining our VC:

If you do not have a SQLPASS account:

a. Go to http://www.sqlpass.org/RegisterforSQLPASS.aspx

b. Fill up the required information and register

Now, After successful login/registration – Go to http://www.sqlpass.org/MYPASS.aspx

a. switch to MyChapters section

b. Now under virtual chapters, you would see a list of virtual chapters. Join the one’s you are interested in!

my PASS my Chapter Azure VC

I look forward to seeing you at next Azure PASS VC’s meeting!