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Azure Weekly: July 8, 2019

Build Azure Weekly provides your go-to source to keep up-to-date on all the latest Microsoft Azure news and updates. Included within Build Azure Weekly newsletter are blog articles, podcasts, videos, and more from Microsoft and the greater community over the past week. Be sure to subscribe to Build Azure Weekly to get the newsletter in your email every week and never miss a thing! The Weeks Top Links Here are some of the most notable links from the week: How to Build Azure The latest addition to BuildAzure.com is the all new “ How to Build Azure ” guides! This is a new section of BuildAzure.com that hosts “How to” style guides to help you be more productive working in the Microsoft Azure cloud. Here’s a few of the latest guides in the How to Build Azure archive: The new “Guide” content type in BuildAzure.com is designed to provide “How to”...

End-to-end CI/CD automation using Azure DevOps unified Yaml-defined Pipelines

Azure pipeline as a core part of Azure DevOps, it allows for the creation of CI ( Continuous Integration) pipeline in a declarative way using YAML documents, it is also called build pipelines. Since last Build 2019, this capability is also extending to CD ( Continuous Delivery ) Pipelines which is also known as Release Pipelines.  More than expected is now it is possible to define multi-stage pipelines-as-code for both Continuous Integration and Continuous Delivery with the same YAML definition file.  Since Github can be easily integrated with Azure DevOps nowadays,  you can not only build your CI/CD pipeline based on your source code on Github, but also even Mapping your GitHub repository permissions with Azure DevOps. Solution Overview I am writing this blog to explain how to use Azure CI/CD pipelines to provide an end-to-end automation experience to users when deploying an node.js application via Azure DevOps. Our solution will...

Scale action groups and suppress notifications for Azure alerts

In Azure Monitor, defining what to monitor while configuring alerts can be challenging. Customers need to be capable of defining when actions and notifications should trigger for their alerts, and more importantly, when they shouldn’t. The action rules feature for Azure Monitor, available in preview, allows you to define actions for your alerts at scale, and allows you to suppress alerts for scenarios such as maintenance windows. Let’s take a closer look at how action rules (preview) can help you in your monitoring setup! Defining actions at scale Previously you could define what action groups trigger for your alerts while defining an alert rule. However, the actions that get triggered, whether it is an email that is sent or a ticket created in a ticketing tool, are usually associated with resource on which the alert is generated rather than the individual alert rule. For example, for all alerts generated on the...

Privacy and AI - How Much Should We Really Care

Summary:  More data means better models but we may be crossing over a line into what the public can tolerate, both in the types of data collected and our use of it.  The public seems divided.  Targeted advertising is good but the increased invasion of privacy is bad.   Headlines are full of alarm.  The public is up in arms.  The internet is stealing their privacy.  Indeed, the Future of Humanity Institute at Oxford rates this as the most severe problem we will face over the next 10 years.   As data scientists how much should we care?  Well more data means better models and less data means less accurate models.  So in a sense the value we bring to the table will be directly impacted if government regulation takes many of our data sources off the table.  So the answer is likely we should care a lot. However, “privacy” has...

Major Factors Keeping Facial Recognition from Mass Adoption

Artificial Intelligence and Machine Learning are accelerating and refining various industries. One of the most rapidly developing and progressive domains is Facial Recognition (FR). Its implementation in many spheres, from public security to retail and healthcare, only proves its potential.     Despite FR’s broad dissemination, there are many precedents where FR still makes mistakes. Media reports are filled with stories of FR’s racial discrimination, for example. The reasons for such failures vary, yet companies already using the technology have hope for its improvement and future benefits.   A National Institute of Standards and Technology research has shown that since 2014 FR technology has been refined more than 20 times . Moreover, according to Allied Market Research, the value of facial recognition technology is likely to rise to $9.6 billion by 2022 with a CAGR of 21.3% between 2016–2022.   While the continued development of the technology seems almost a given...

Constructing Role Objects and Interpreting Role Conflicts Through the Lens of Stress

In my previous post , I discussed the relationship between role conflict and performance.  I suggested that all things being equal, role conflict might be the primary determinant of employee performance.  Companies direct all sorts of resources gathering data for recruitment purposes.  All things being about the same, much of that data collection is irrelevant.  This means that if a pool of recruitment prospects is relatively homogeneous in terms of their abilities, the balance of analysis should be focused on role conflict.  In this blog, I will consider the structure of role objects and the perspective of the stress lens. A role conflict occurs when two roles conflict.  For my model, each role object that a person has contains two components: 1) gates or the role prerequisites; and 2) traps or the role barriers.  An individual carries a persona containing a number of different roles.  When a particular gate is found...

Co-integration and Structural Breaks Time Series Analysis using R on 100 year bond yields

Co-Integration in Time Series Analysis is when one data points is depended on other data points or follow the pattern. Example in capital markets Industry or sector leader company stock leads the direction and many small companies follows it. Example : Crude oil and Gasoline prices. Price of gasoline is dependent on Crude oil prices. Here Crude oil price always drives gasoline prices.  To analyze similar co-integration used Moody's corporate AAA and BBB Bond Yields. Corporate bond BBB yields are co-integrated with yields of AAA.  These Bond yield prices are downloaded from FRED economic data St. Louis using getSymbols() function from package quantmod. This downloaded data is from 1920 to 2019  about 100 years.    After plotting it is clearly visible how bond yields are co-integrated .  Before plotting downloaded data is converted to time series using ts() function.                         ...

Scaling Innovation:  Whiteboards versus Maps

I love watching the NBA’s Golden State Warriors play basketball. Their offensive “improvisation” is a thing of beauty in their constant ball movement in order to find the “best” shot. They are a well-oiled machine optimizing split-second decisions in an ever-changing landscape that is heavily influenced by questions such as: Who is my defender?What are the strengths of my defender?From where is help likely to come if I make a move to the basket?Who is likely to be open if help does come?Who has a defensive mismatch?Who is hot?What’s the game situation?What is the shot clock status?Is this the “best” shot or should I keep looking? The coordinated decision-making is truly a thing of beauty, but here’s the challenge: how would you “scale” the Warriors? You can’t just add another player to the mix – even a perennial all-star like Boogie Cousins – and have the same level of success. One...

Deploying Python application using Docker and AWS

The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline. Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly. Whatever the reason, Docker alleviates this situation by storing the necessary components in an image, which can then be used by a third-party to deploy an application effectively. In this example, we will see how a simple Python script can be incorporated into a Docker image, and this image will then be pushed to ECR (Elastic Container Registry) in AWS. Python Script Consider a simple Python script for calculating a cumulative binomial...

28 Statistical Concepts Explained in Simple English - Part 18

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles,  sign up on DSC . Below is the last article in the series Statistical Concepts Explained in Simple English. The full series is accessible  here .  Source for picture: here 28 Statistical Concepts Explained in Simple English - Part 18 Unidimensionality: Definition, Examples Uniform Distribution / Rectangular Distribution: What is it? Unimodal Distribution in Statistics Unit Root: Simple Definition, Unit Root Tests Univariate Analysis: Definition, Examples Upper and Lower Fences Upper Hinge and Lower Hinge Validity Coefficient: Definition and How to Find it Variability in Statistics: Definition, Examples Variance: Simple Definition, Step by Step Examples Variance Inflation Factor Voluntary Response Sample in Statistics: Definition Wald Test: Definition,...