As organizations start to create and maintain clusters in AKS (Azure Kubernetes Service), they also need to use cloud-based identity and access management service to access other Azure cloud resources and services. The Azure Active Directory (AAD) pod identity is a service that gives users this control by assigning identities to individual pods.  

Without these controls, accounts may get access to resources and services they don’t require. And it can also become hard for IT teams to track which set of credentials were used to make changes.

Azure AD Pod identity is just one small part of the container and Kubernetes management process and as you delve deeper, you will realize the true power that Kubernetes and Containers bring to your DevOps ecosystem.

Here is a more detailed look at how to use AAD pod identity for connecting pods in AKS cluster with Azure Key Vault.

Pod Identity

Integrate your key management system with Kubernetes using pod identity. Secrets, certificates, and keys in a key management system become a volume accessible to pods. The volume is mounted into the pod, and its data is available directly in the container file system for your application.

On an existing AKS cluster –

Deploy Key Vault FlexVolume to your AKS cluster with this command:

  • kubectl create -f
1. Create the Deployment

Run this command to create the aad-pod-identity deployment on an RBAC-enabled cluster:

  • kubectl apply -f

Or run this command to deploy to a non-RBAC cluster:

  • kubectl apply -f
2. Create an Azure Identity

Create azure managed identity

Command:- az identity create -g ResourceGroupNameOfAKsService -n aks-pod-identity(ManagedIdentity)


"clientId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx ",
"clientSecretUrl": " ",
"id": "/subscriptions/xxxxxxxx-xxxx-XXXX-XXXX-XXXXXXXXXXXX/resourcegroups/aks_dev_rg_wu/providers/Microsoft.ManagedIdentity/userAssignedIdentities/aks-pod-identity",
"location": "westus",
"name": "aks-pod-identity",
"resourceGroup": "au10515_aks_dev_rg_wu",
"tags": {},
"type": "Microsoft.ManagedIdentity/userAssignedIdentities"

Assign Cluster SPN Role

Command for Getting AKSServicePrincipalID:- az aks show -g <resourcegroup> -n <name> –query servicePrincipalProfile.clientId -o tsv

Command:-az role assignment create –role “Managed Identity Operator” –assignee <AKSServicePrincipalId> –scope < ID of Managed identity>

Assign Azure Identity Roles

Command:- az role assignment create –role Reader –assignee <Principal ID of Managed identity> –scope <KeyVault Resource ID>

Set policy to access keys in your Key Vault

Command:- az keyvault set-policy -n dev-kv –key-permissions get –spn  <Client ID of Managed identity>

Set policy to access secrets in your Key Vault

Command:- az keyvault set-policy -n dev-kv –secret-permissions get –spn <Client ID of Managed identity>

Set policy to access certs in your Key Vault

Command:- az keyvault set-policy -n dev-kv –certificate-permissions get –spn <Client ID of Managed identity>

3. Install the Azure Identity

Save this Kubernetes manifest to a file named aadpodidentity.yaml:

apiVersion: ""
kind: AzureIdentity
name: <a-idname>
type: 0
ResourceID: /subscriptions/<subid>/resourcegroups/<resourcegroup>/providers/Microsoft.ManagedIdentity/userAssignedIdentities/<name>
ClientID: <clientId>

Replace the placeholders with your user identity values. Set type: 0 for user-assigned MSI or type: 1 for Service Principal.

Finally, save your changes to the file, then create the AzureIdentity resource in your cluster:

kubectl apply -f aadpodidentity.yaml

4. Install the Azure Identity Binding

Save this Kubernetes manifest to a file named aadpodidentitybinding.yaml:

apiVersion: ""
kind: AzureIdentityBinding
  name: demo1-azure-identity-binding
  AzureIdentity: <a-idname>
  Selector: <label value to match>

Replace the placeholders with your values. Ensure that the AzureIdentity name matches the one in aadpodidentity.yaml.

Finally, save your changes to the file, then create the AzureIdentityBinding resource in your cluster:

kubectl apply -f aadpodidentitybinding.yaml

Sample Nginx Deployment for accessing key vault secret using Pod Identity

Save this sample nginx pod manifest file named nginx-pod.yaml:

apiVersion: v1
kind: Pod
    app: nginx-flex-kv-podid
  name: nginx-flex-kv-podid
  - name: nginx-flex-kv-podid
    image: nginx
    - name: test
      mountPath: /kvmnt
      readOnly: true
  - name: test
      driver: "azure/kv"
        usepodidentity: "true"         # [OPTIONAL] if not provided, will default to "false"
        keyvaultname: ""               # the name of the KeyVault
        keyvaultobjectnames: ""        # list of KeyVault object names (semi-colon separated)
        keyvaultobjecttypes: secret    # list of KeyVault object types: secret, key or cert (semi-colon separated)
        keyvaultobjectversions: ""     # [OPTIONAL] list of KeyVault object versions (semi-colon separated), will get latest if empty
        resourcegroup: ""              # the resource group of the KeyVault
        subscriptionid: ""             # the subscription ID of the KeyVault
        tenantid: ""            # the tenant ID of the KeyVault
Azure AD Pod Identity points to remember when implementing in cluster
  • Azure AD Pod Identity is currently bound to the default namespace. Deploying an Azure Identity and it’s binding to other namespaces, will not work!
  • Pods from all namespaces can be executed in the context of an Azure Identity deployed to the default namespace (related to point 1)
  • Every Pod Developer can add the aadpodidbinding label to his/her pod and use your Azure Identity
  • Azure Identity Binding is not using default Kubernetes label selection mechanism



Cloud Foundry has a Container-based architecture, open source cloud application platform. It provides the cloud instances and mainly used to deploy the Application directly into cloud environment. Instead of running the app separately, using the CF CLI(Command Line Interface) tool to deploy , test, configure and manage the apps on CF.

Features of Cloud Foundry:
  • An open source Cloud Native Platform
  • Fast and easy to build, test, deploy ,manage& scale apps
  • Works with any language or framework
  • Highly adaptable
  • Can able to see running status of apps
  • Can scale up or down, debug apps on CF
How to interact with CF?
  • Command Line Interface (CLI): from terminal / command prompt
  • IDE plugins
Org and App Space Roles:

CF uses role-based access control, with each role granting permissions in either an organization or an application space.

Organisation :
  • An Organisation or org represents an organisational account and groups together users, resources, applications, and environments.
  • Each organisation has a resource quota and it shares the same resource and domain.
  • Organisations segregate tenants in a Cloud Foundry installation.

To List all orgs that the user has access to the below command can be given in the terminal.

cf orgs
  • An organisation have separate spaces for development, staging and production versions of the apps.
  • A space can also have its own quota.
  • It has the shared location for developing and running apps
  • Every application and service is scoped to a space

To List all spaces in the current org

cf spaces
Relationship between org, space and Apps:
Before pushing the app into Cloud Foundry, Ensure that:
  • Log into cloud foundary using cf login command
    • cf login -a API-URL
  • It will prompt for username and password, then give the correct credentials
  • Select the org and space where the app gets push.
  • Then push the application using cf push
How to deploy an app into cf?

To deploy an application, need to push its code to the Cloud Foundry instance. The push command is used to push the application on cloud foundary. The arguments may be vary depends on application types. However, it is the best practice to specify all the arguments in a system file called manifest.yml

It provides consistency and reproducibility.An app can specify its service instance dependencies in the manifest.yml file. It will automatically bind to the service instances.

  1. # Start a new app called “myapp”
  2. # If there’s a manifest.yml in the current folder,
  3. # the config will be read from there
  4. cf push
Manifest Format

Manifests has written in YAML. The below manifest illustrates some YAML conventions, as follows:

  • The manifest file begins with three dashes.
  • The applications block begins with a heading followed by a colon.
  • The app name is preceded by a single dash and one space.
  • Subsequent lines in the block are indented two spaces to align with name
Sample manifest.yml


– name: my-app

memory: 512M

instances: 2


  • A Cloud Foundry component that resolves app’s runtime dependencies
  • It provides framework and run time support for applications.
  • It is used to determine what dependencies to download
  • It is used to tell how to configure applications to communicate with different services.
  • It is used to compile or prepare the application for launch.
What happens when push an app using cf push?
  • Upload: App files sent to CF
  • Staging:Executable artifact is created (droplet)
  • Running:App starts on an app host

App receives web requests (if it binds to TCP port)

List of cf commands:
cf commandsPurpose
cf targetSets or views the targeted organization or space
cf stopStops an application
cf startStart an app
cf set-envSets an environment variable for an application(cf set-env var_name var_value)
cf servicesLists all of the services that are available in the current space
cf restartStop all instances of the app, then start them again. This causes downtime.
cf restageRecreate the app’s executable artifact using the latest pushed app files and the latest environment (variables, service bindings, buildpack, stack, etc.). This action will cause app downtime.
cf renameRename an app
cf pushDeploys a new application(cf push )
cf marketplaceLists all of the services that are available in the marketplace.
cf logsDisplays the STDOUT and STDERR log streams of an application.(cf logs
cf login -a Log in to CF
cf helpshow help
cf eventsDisplays runtime events that are related to an application.(cf events )
cf deleteDeletes an existing application.(cf delete
cf create-spaceCreates a space.(cf create-space
cf bind-serviceBinds an existing service instance to your application.
cf appsLists all of the applications that you deployed in the current space. The status of each application is also displayed.
cf apiTo view the current API endpoint
cf -vDisplays the version of the Cloud Foundry command line interface.

What is Cross Browser Testing?

Cross Browser Testing is a type of Functional Test to check whether web application works as expected on different browsers.


Cross-browser testing is basically running the same set of test cases multiple times on different browsers.

Below two are the most intent of cross-browser testing,

Below two are the most intent of cross-browser testing,
  1. Below two are the most intent of cross-browser testing,
  2. Appearance of the page in different browsers- is it the same, is it different, if one is better than the other, etc

Note: In recent years, testing mobile browsers are included on the Cross-Browser testing scope.

When this testing can be started?

Any testing reaps the best benefits when it is done early on. Therefore, the industry recommendation is to start with it as soon as the page designs are available. Because finding and fixing bugs on early stages are very cost effective. Finding bugs after release or completion of application will not be a cost effective one.

Cross Browser testing through Manual:

Sure, it can be done manually. First, business needs to identify all browsers that the application needs to support. Tester need to run all the testcase against every identified browser and observe whether the appearance and functionality are same.

Through manual testing, it is not possible to cover many browsers and its major versions. So, performing cross browser testing manually will be costly and time-consuming too.

In an Agile world it’s not a good advice to do whole cross browser testing through manual.

Cross Browser testing through Automation:

As stated above, Cross-browser testing is basically running the same set of test cases multiple times on different browsers. This type of repeated task is best suited for automation. Thus, it’s more cost and time effective to perform this testing by using tools.

Selenium for Cross Browser Testing:

Selenium is well known for automated testing of the web-based applications. Just by changing the browser to be used for running the test cases, selenium makes it very easy to run the same test cases multiple times using different browsers.

Note: Rest of this blog we are going to see how Selenium can be used for Cross-Browser Testing.

Advantages of choosing Selenium:
  • Open source
  • Supports programming languages like Java, Perl, Python, C#, Ruby, Groovy, Java Script, etc
  • Platform Independent: Supports (OS) like Windows, Mac, Linux, UNIX, etc.
  • Supports multiple browsers namely, Internet Explorer, Chrome, Firefox, Opera, Safari, etc
  • Ease of implementation
  • Reusability

By using TestNG along with Selenium Grid we can achieve parallel test execution on different browser in different machines. Let’s see TestNG and Selenium Grid on the following topics,


TestNG is an automation testing framework in which NG stands for “Next Generation”. TestNG is inspired from JUnit which uses the annotations (@). Default Selenium tests do not generate a proper format for the test results. Using TestNG we can generate test results.

Why TestNG?
  • Multiple test cases can be grouped easily by converting them into testng.xml file. In which you can make priorities which test case should be executed first.
  • The same test case can be executed multiple times without loops just by using keyword called ‘invocation count.’
  • Using TestNG, you can execute multiple test cases on multiple browsers
  • It can be easily integrated with tools like Maven, Jenkins, etc.
Selenium Grid

Selenium Grid is a part of the Selenium Suite which specialise in running multiple tests across different browsers, operating system and machines. You can connect to it with Selenium Remote by specifying the browser, browser version, and operating system you want

Components of Selenium Grid

In Selenium Grid, the HUB is a computer which is the central point where we can load our tests into. Hub also acts as a server because of which it acts as a central point to control the network of Test machines. The Selenium Grid has only one hub and it is the master of the network.


In Selenium Grid, a NODE is referred to a Test Machine which opts to connect with the Hub. This test machine will be used by Hub to run tests on. A Grid network can have multiple nodes. A node is supposed to have different platforms i.e. different operating system and browsers. The node does not need the same platform for running as that of hub.

Advantages of Selenium Grid
  • Selenium Grid allows running multiple tests across different web browsers, operating systems, and machines. This ensures compatibility of the application under test across multiple combinations of web browsers, operating system, and hardware architecture
  • It speeds up the test suite completion time as it can run multiple tests in parallel. For example, if we have 10 nodes and we need to execute a test suite of 50 tests then it is going to take 10 times lesser time than a single machine that runs this test suit without Selenium Grid.
Disadvantage of Selenium Grid
  • Extra cost to project as it requires additional machines as Nodes
Grid Code Snippets:

What is Jenkins?

Jenkins is an open source automation tool written in Java with plugins built for Continuous Integration purpose. Jenkins is used to build and test your software projects continuously making it easier for developers to integrate changes to the project, and making it easier for users to obtain a fresh build. It also allows you to continuously deliver your software by integrating with a large number of testing and deployment technologies.

With Jenkins, organizations can accelerate the software development process through automation. Jenkins integrates development life-cycle processes of all kinds, including build, document, test, package, stage, deploy, static analysis and much more.

Jenkins achieves Continuous Integration with the help of plugins. Plugins allows the integration of Various DevOps stages. If you want to integrate a particular tool, you need to install the plugins for that tool. For example: Git, Maven 2 project, Amazon EC2, HTML publisher etc.

Advantages of Jenkins include:

  • It is an open source tool with great community support.
  • It is easy to install.
  • It has 1000+ plugins to ease your work. If a plugin does not exist, you can code it and share with the community.
  • It is free of cost.
  • It is built with Java and hence, it is portable to all the major platforms
What is Continuous Integration?

Continuous Integration is a development practice in which the developers are required to commit changes to the source code in a shared repository several times a day or more frequently. Every commit made in the repository is then built. This allows the teams to detect the problems early. Apart from this, depending on the Continuous Integration tool, there are several other functions like deploying the build application on the test server, providing the concerned teams with the build and test results etc.

Continuous Integration with Jenkins
  • First, a developer commits the code to the source code repository. Meanwhile, the Jenkins server checks the repository at regular intervals for changes.
  • Soon after a commit occurs, the Jenkins server detects the changes that have occurred in the source code repository. Jenkins will pull those changes and will start preparing a new build.
  • If the build fails, then the concerned team will be notified.
  • If built is successful, then Jenkins deploys the built in the test server.
  • After testing, Jenkins generates a feedback and then notifies the developers about the build and test results.
  • It will continue to check the source code repository for changes made in the source code and the whole process keeps on repeating.
Jenkins Distributed Architecture

Jenkins uses a Master-Slave architecture to manage distributed builds. In this architecture, Master and Slave communicate through TCP/IP protocol.

Jenkins Master

Your main Jenkins server is the Master. The Master’s job is to handle:

  • Scheduling build jobs.
  • Dispatching builds to the slaves for the actual execution.
  • Monitor the slaves (possibly taking them online and offline as required).
  • Recording and presenting the build results.
  • A Master instance of Jenkins can also execute build jobs directly.
Jenkins Slave

A Slave is a Java executable that runs on a remote machine. Following are the characteristics of Jenkins Slaves:

  • It hears requests from the Jenkins Master instance.
  • Slaves can run on a variety of operating systems.
  • The job of a Slave is to do as they are told to, which involves executing build jobs dispatched by the Master.
  • You can configure a project to always run on a particular Slave machine, or a particular type of Slave machine, or simply let Jenkins pick the next available Slave.
What is a Jenkins pipeline?

A pipeline is a collection of jobs that brings the software from version control into the hands of the end users by using automation tools. It is a feature used to incorporate continuous delivery in our software development workflow.

Over the years, there have been multiple Jenkins pipeline releases including, Jenkins Build flow, Jenkins Build Pipeline plugin, Jenkins Workflow, etc. What are the key features of these plugins?

  • They represent multiple Jenkins jobs as one whole workflow in the form of a pipeline.
  • What do these pipelines do? These pipelines are a collection of Jenkins jobs which trigger each other in a specified sequence.

Lets look at an example. Suppose I’m developing a small application on Jenkins and I want to build, test and deploy it. To do this, I will allot 3 jobs to perform each process. So, job1 would be for build, job2 would perform tests and job3 for deployment. I can use the Jenkins build pipeline plugin to perform this task. After creating three jobs and chaining them in a sequence, the build plugin will run these jobs as a pipeline.

This approach is effective for deploying small applications. But what happens when there are complex pipelines with several processes (build, test, unit test, integration test, pre-deploy, deploy, monitor) running 100’s of jobs?

The maintenance cost for such a complex pipeline is huge and increases with the number of processes. It also becomes tedious to build and manage such a vast number of jobs. To overcome this issue, a new feature called Jenkins Pipeline Project was introduced.

The key feature of this pipeline is to define the entire deployment flow through code. What does this mean? It means that all the standard jobs defined by Jenkins are manually written as one whole script and they can be stored in a version control system. It basically follows the ‘pipeline as code’ discipline. Instead of building several jobs for each phase, you can now code the entire workflow and put it in a Jenkinsfile. Below is a list of reasons why you should use the Jenkins Pipeline.

Jenkins Pipeline Advantages
  • It models simple to complex pipelines as code by using Groovy DSL (Domain Specific Language)
  • The code is stored in a text file called the Jenkinsfile which can be checked into a SCM (Source Code Management)
  • Improves user interface by incorporating user input within the pipeline
  • It is durable in terms of unplanned restart of the Jenkins master
  • It can restart from saved checkpoints
  • It supports complex pipelines by incorporating conditional loops, fork or join operations and allowing tasks to be performed in parallel
  • It can integrate with several other plugins
What is a Jenkinsfile?

A Jenkinsfile is a text file that stores the entire workflow as code and it can be checked into a SCM on your local system. How is this advantageous? This enables the developers to access, edit and check the code at all times.

The Jenkinsfile is written using the Groovy DSL and it can be created through a text/groovy editor or through the configuration page on the Jenkins instance. It is written based on two syntaxes, namely:

  • Declarative pipeline syntax
  • Scripted pipeline syntax

Declarative pipeline is a relatively new feature that supports the pipeline as code concept. It makes the pipeline code easier to read and write. This code is written in a Jenkinsfile which can be checked into a source control management system such as Git.

Whereas, the scripted pipeline is a traditional way of writing the code. In this pipeline, the Jenkinsfile is written on the Jenkins UI instance. Though both these pipelines are based on the groovy DSL, the scripted pipeline uses stricter groovy based syntaxes because it was the first pipeline to be built on the groovy foundation. Since this Groovy script was not typically desirable to all the users, the declarative pipeline was introduced to offer a simpler and more optioned Groovy syntax.

The declarative pipeline is defined within a block labelled ‘pipeline’ whereas the scripted pipeline is defined within a ‘node’

An example Jenkinsfile looks like this:

pipeline {
environment {
agent any
stages {
stage('Checkout: Code') {
steps {
sh "mkdir -p $WORKSPACE/repo;\
git config --global '';\
git config --global 'myname';\
git config --global push.default simple;\
sh "chmod -R +x $WORKSPACE/repo/$BUILD_SCRIPTS"
stage('Yum: Updates') {
steps {
sh "sudo chmod +x $WORKSPACE/repo/$BUILD_SCRIPTS/scripts/"
sh "sudo $WORKSPACE/repo/$BUILD_SCRIPTS/scripts/"
post {
always {

The above Jenkins file does the following.

  • sets up environment variables
  • pulls data down from a git repo
  • sets it up in a Jenkins workspace
  • runs a script under scripts/
  • once completes by cleaning up the workspace (successful or not)
Pipeline concepts
  • Pipeline

This is a user defined block which contains all the processes such as build, test, deploy, etc. It is a collection of all the stages in a Jenkinsfile. All the stages and steps are defined within this block. It is the key block for a declarative pipeline syntax.

  • Node

A node is a machine that executes an entire workflow. It is a key part of the scripted pipeline syntax.

There are various mandatory sections which are common to both the declarative and scripted pipelines, such as stages, agent and steps that must be defined within the pipeline. These are explained below:

  • Agent

An agent is a directive that can run multiple builds with only one instance of Jenkins. This feature helps to distribute the workload to different agents and execute several projects within a single Jenkins instance. It instructs Jenkins to allocate an executor for the builds.

A single agent can be specified for an entire pipeline or specific agents can be allotted to execute each stage within a pipeline. Few of the parameters used with agents are:

  • Any

Runs the pipeline/ stage on any available agent.

  • None

This parameter is applied at the root of the pipeline and it indicates that there is no global agent for the entire pipeline and each stage must specify its own agent.

  • Label

Executes the pipeline/stage on the labelled agent.

  • Docker

This parameter uses docker container as an execution environment for the pipeline or a specific stage. In the below example I’m using docker to pull an ubuntu image. This image can now be used as an execution environment to run multiple commands.

  • Stages

This block contains all the work that needs to be carried out. The work is specified in the form of stages. There can be more than one stage within this directive. Each stage performs a specific task. In the following example, I’ve created multiple stages, each performing a specific task.

  • Steps

A series of steps can be defined within a stage block. These steps are carried out in sequence to execute a stage. There must be at least one step within a steps directive. In the following example I’ve implemented an echo command within the build stage. This command is executed as a part of the ‘Build’ stage.

Continuous Integration (CI) is a development practice where developers integrate code into a shared repository frequently, preferably several times a day. Each integration can then be verified by an automated build and automated tests. While automated testing is not strictly part of CI it is typically implied.

One of the key benefits of integrating regularly is that you can detect errors quickly and locate them more easily. As each change introduced is typically small, pinpointing the specific change that introduced a defect can be done quickly.

In recent years CI has become a best practice for software development and is guided by a set of key principles. Among them are revision control, build automation and automated testing.

Benefits and Advantages of Continuous Integration

Continuous Integration has many benefits. A good CI setup speeds up your workflow and encourages the team to push every change without being afraid of breaking anything. There are more benefits to it than just working with a better software release process. Continuous Integration brings great business benefits as well.

  • Reduces the time and effort for integrations of different code changes
  • Enables a quick feedback mechanism on every change
  • Allows earlier detection and prevention of defects
  • Helps collaboration between team members so recent code is always shared
  • Reduces manual testing effort
  • Building features more incrementally saves time on the debugging side so you can focus on adding features
  • First step into fully automating the whole release process
  • Prevents divergence in different branches as they are integrated regularly
Continuous Integration Tools


Jenkins is a cross-platform open source CI tool written in Java. It offers configuration through both the GUI interface and the console commands. Jenkins is a very flexible tool to use because it offers an extension of features through plugins. Its plugin list is very broad, and one can easily add their own plugins to that list. Furthermore, Jenkins can distribute software builds and test loads on several machines.

Travis CI

Travis CI is an open source CI service free for all open source projects hosted on GitHub. Since Travis CI is hosted, it is platform independent. It is configured using Travis.Yml files which contain actionable data. Travis CI supports a variety of software languages, and the build configuration for each of those languages is complete. Travis CI uses virtual machines to create applications.


TeamCity is a Java-based sophisticated CI tool offered by JetBrains. It supports Java,Net and Ruby platforms. TeamCity has a range of free plugins available developed both by JetBrains and third parties. It also offers integration with several IDEs including, Eclipse, IntelliJ IDEA and Visual Studio. Moreover, TeamCity allows simultaneous running of multiple builds and tests in different platforms and environments.

GitLab CI

GitLab CI is hosted on the free hosting service, and it offers Git repository management function with features such as, access control, bug tracking, and code reviewing. GitLab CI is completely unified with GitLab and it can easily be used to link projects using the GitLab API. GitLab CI process builds are coded in the Go language and can execute on several operating systems such as, Windows, Linux, Docker, OSX, and FreeBSD.


CircleCI is a CI tool hosted only on GitHub. It supports several languages, including Java, Python, Ruby/Rails, Node.js, PHP, Skala and Haskell. It offers services based on containers. CircleCI offers one container free, and any number of projects can be built on it. It offers up to five levels of parallelization (1x, 4x, 8x, 12x and 16x). Therefore, maximum parallelization of 16x can be achieved in one build. CircleCI also supports Docker platform.


Bamboo is a CI tool developed by Atlassian. Bamboo is available in two versions, cloud and server. For the cloud version, Atlassian offers hosting service with the help of Amazon EC2 account. For the server version, self-hosting needs to be done. Bamboo supports well known Atlassian products, JIRA and BitBucket.

Machine Learning

Artificial Intelligence

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

In Traditional Programming, data and program are run on the computer to produce the output. In Machine Learning, data and output are run on the computer to create a program. The program can be used in traditional programming.

Machine learning algorithms are often categorized as supervised or unsupervised.

Supervised Learning

Supervised learning is a learning in which we teach or train the machine using data which is well labelled that means some data is already tagged with correct answer. After that, machine is provided with new set of examples(data) so that supervised learning algorithm analyses the training data (set of training examples) and produces a correct outcome from labelled data.

Classification algorithms and regression algorithms are types of supervised learning. Classification algorithms are used when the outputs are restricted to a limited set of values. For a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email. For an algorithm that identifies spam emails, the output would be the prediction of either “spam” or “not spam”, represented by the Boolean values true and false. Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.

Unsupervised Learning

Unsupervised learning is the training of machine using information that is neither classified nor labelled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data. The most common unsupervised learning method is cluster analysis or clustering, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Some simple Machine Learning algorithms

Linear Regression

Here, we establish a relationship between independent and dependent variables by fitting the best line. It is used to estimate real values (cost of houses, number of calls, total sales, etc.) based on a continuous variable(s).

Below model is used to predict the Ice cream sales based on the temperature in a city.

We need a weight(w) and a bias(b) to fit a straight-line (y = wx + b) and this can be diagrammatically represented as given below:

Above diagram is the simplest Neural Network. A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.

Logistic Regression

Logistic Regression is a classification algorithm used to estimate discrete binary values (like 0/1, yes/no, true/false) based on given set of independent variables. Typically, this involves fitting a curve to separate 2 distinct classes of data points.

The neural network for logistic regression has multiple weights / bias as inputs and 2 output nodes as shown below:

Deep Learning

Deep learning is a specific method of machine learning, and it’s based primarily on the use of neural networks.

In traditional supervised machine learning, systems require an expert to use his or her domain knowledge to specify the information (called features) in the input data that will best lead to a well-trained system. In Deep Learning, rather than specifying the features in our data that we think will lead to the best classification accuracy, we let the machine find this information on its own. Often, it can look at the problem in a way that even an expert wouldn’t have been able to imagine.

Neural Network Terminology

Activation function

The activation function of a node defines the output of that node, or “neuron”, given an input or set of inputs. This output is then used as input for the next node and so on until a desired solution to the original problem is found. Some of the commonly used activation functions are given below

Input / Output / Hidden Layers

Simply as the name suggests the input layer is the one which receives the input and is essentially the first layer of the network. The output layer is the one which generates the output or is the final layer of the network. The processing layers are the hidden layers within the network. These hidden layers are the ones which perform specific tasks on the incoming data and pass on the output generated by them to the next layer. The input and output layers are the ones visible to us, while are the intermediate layers are hidden.

Forward propagation

Forward Propagation refers to the movement of the input through the hidden layers to the output layers. In forward propagation, the information travels in a single direction FORWARD. The input layer supplies the input to the hidden layers and then the output is generated. There is no backward movement.

Cost / Loss function

When we build a network, the network tries to predict the output as close as possible to the actual value. We measure this accuracy of the network using the loss function. The loss function tries to penalize the network when it makes errors. Our objective while running the network is to increase our prediction accuracy and to reduce the error, hence minimizing the loss function. The most optimized output is the one with the least value of the loss function. If we define the loss function to be the mean squared error, it can be written as –

C= 1/m ∑ (y – a)2 where m is the number of training inputs, a is the predicted value and y is the actual value of that example.

The learning process revolves around minimizing the cost.

Gradient Descent

Gradient descent is an optimization algorithm for minimizing the cost. To think of it intuitively, while climbing down a hill you should take small steps and walk down instead of just jumping down at once. Therefore, what we do is, if we start from a point x, we move down a little i.e. delta h, and update our position to x-delta h and we keep doing the same till we reach the bottom. Consider bottom to be the minimum cost point.

Mathematically, to find the local minimum of a function one takes steps proportional to the negative of the gradient of the function.

Learning Rate

rate at which we descend towards the minima of the cost function is the learning rate. We should choose the learning rate very carefully since it should neither be very large that the optimal solution is missed and nor should be very low that it takes forever for the network to converge.


When we define a neural network, we assign random weights and bias values to our nodes. Once we have received the output for a single iteration, we can calculate the error of the network. This error is then fed back to the network along with the gradient of the cost function to update the weights of the network. These weights are then updated so that the errors in the subsequent iterations is reduced. This updating of weights using the gradient of the cost function is known as back-propagation.

Steps in training a Neural Network
  • Initialize weights and biases.
  • ii. Forward propagation: Using the input X, weights W and biases b, for every layer we compute Z and A, the Linear and Non-linear activations. At the final layer, we compute f(A^(L-1)) which could be a sigmoid, softmax or linear function of A^(L-1) and this gives the prediction y_hat.
  • Compute the loss function: This is a function of the actual label y and predicted label y_hat. It captures how far off our predictions are from the actual target. Our objective is to minimize this loss function.
  • Backward Propagation: In this step, we calculate the gradients of the loss function f(y, y_hat) with respect to A, W, and b called dA, dW and db. Using these gradients, we update the values of the parameters from the last layer to the first.
  • Repeat steps 2–4 for n iterations/epochs till we feel we have minimized the loss function, without overfitting the train data
Machine Learning using Python

Simple Machine Learning models like Linear Regression can be trained using the python library scikit-learn. Neural Networks are built and trained using the libraries Keras, TensorFlow or PyTorch.

In below simple example, we are building a linear regression model to predict the ice cream sales based on temperature. 80% of the available data is used for testing and we are using the remaining 20% data for testing our model.

import matplotlib.pyplot as plt   
import numpy as np   
from sklearn.linear_model import LinearRegression  
from sklearn.metrics import r2_score  
import pandas as pd  
 # load the dataset   
 Stock_Market = {'Temprature_in_Fahrenheit' :[58, 62, 52, 60, 66, 74, 68, 80, 76, 74, 64,],  
 'Ice_Cream_sales': [215,325,185,332,406,522,412,614,544,44500000,408]          
 df = pd.DataFrame(Stock_Market,columns=['Temprature_in_Fahrenheit','Ice_Cream_sales'])  
 X = df[['Temprature_in_Fahrenheit']]  
 Y = df['Ice_Cream_sales']  
 # splitting X and y into training and testing sets   
 from sklearn.model_selection import train_test_split   
 X_train, X_test, y_train, y_test = train_test_split(X, Y, 
 test_size=0.2, random_state=1)
 # create linear regression object   
 reg = LinearRegression()  
 # train the model using the training sets, y_train)  
  y_predict = reg.predict(X_test)  
  ## plotting residual errors in training data   
  plt.scatter(reg.predict(X_train), reg.predict(X_train) - 
  y_train, color = "green", s = 10, label = 'Train data')   
  ## plotting residual errors in test data   
  plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, 
  color = "blue", s = 10, label = 'Test data')   
  ## plotting line for zero residual error   
  plt.hlines(y = 0, xmin = 0, xmax = 2000, linewidth = 2)   
  ## plotting legend   
  plt.legend(loc = 'upper right')   
  ## plot title   
  plt.title("Residual errors")     
  ## function to show plot  


What is RabbitMQ?

RabbitMQ is an open source message broker software. It accepts messages from producers, and delivers them to consumers. It acts like a middleman which can be used to reduce loads and delivery times taken by web application servers

Features of RabbitMQ:

RabbitMQ is an open source message broker software. It accepts messages from producers, and delivers them to consumers. It acts like a middleman which can be used to reduce loads and delivery times taken by web application servers

  • Robust messaging for building applications in a distributed manner.
  • Easy to use
  • Runs on all major Operating Systems.
  • Supports a huge number of developer platforms
  • Supports multiple messaging protocols, message queuing, delivery acknowledgement, flexible routing to queues, multiple exchange type.
  • Open source and commercially supported
How RabbitMQ Works?

The Producer sends messages to an exchange. An exchange is responsible for the routing of the messages to the different queues. An exchange accepts messages from the producer application and routes them to message queues with the help of bindings and routing keys. A binding is a key between a queue and an exchange. Then consumers receive messages from the queue.

  • RabbitMQ
  • Python

How to Send and Receive a message using RABBITMQ?

Send a Message using RabbitMQ:

Following Program will send a single message to the queue.

Step 1: To Establish a connection with RabbitMQ server.

        import pika
        connection = pika.BlockingConnection(
        channel =

Step 2: To Create a hello queue to which the message will be delivered:


Step 3: Publish the message and mention the exchange details and queue name in the exchange and routing key params to which queue the message should go.

channel.basic_publish(exchange='', routing_key='hello', body='Hello RabbitMQ!')
        print(" [x] Sent 'Hello RabbitMQ!'")

Step 4: closing a connection to make sure the network buffers were flushed and our message was actually delivered to RabbitMQ

Recieve a message using RabbitMQ:

Following Program will send a single message to the queue.

Step 1: It works by subscribing a callbackfunction to a queue. Whenever receiving a message, this callback function is called by the Pika library. Following function will print on the screen the contents of the message.

def callback(ch, method, properties, body):
print(" [x] Received %r" % body)

Step 2:Next, need to tell RabbitMQ that this particular callback function should receive messages from our hello queue:

queue='hello', on_message_callback=callback, auto_ack=True)


Step 3:And finally, Enter a never-ending loop that waits for data and runs callbacks whenever necessary.

print(' [*] Waiting for messages. To exit press CTRL+C')

Step 4:Open terminal. Run the The producer program will stop after every run: python [x] Sent ‘Hello RabbitMQ!’ We can go to the web browser and hit the URL http://localhost:15672/, and see the count of the message sent as shown below in the dashboard:

Step 5: Open terminal. Run the program.

[*] Waiting for messages. To exit press CTRL+C
[x] Received ‘Hello RabbitMQ!’

If ready and total count is zero in the dashboard, then confirm the messages are received by consumer.

Note: Continuously send a message through RabbitMQ. As noticed, the program doesn’t exit. It will stay ready to receive further messages, and may be interrupted with Ctrl-C.

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