sneha cynix


Microsoft Azure - Deploying Virtual Machines

Quick Create
Step 1 − Login to Azure Management Portal.

Step 2 − Locate and click on ‘Virtual Machines’ in the left panel and then click on ‘Create a Virtual Machine’.

Step 3 − Alternatively, click ‘New’ at the bottom left corner and then click ‘Compute’ → ‘Virtual Machine’ →‘Quick Create’.

Step 4 − Enter DNS name. This has to be unique. The DNS name is used to connect to the virtual machine.

Step 5 − Select the image and size from the dropdown list. The size affects the cost of running virtual machine.

Step 6 − Enter username and password. You must remember to log in to the virtual machine later.

Step 7 − Select the relevant region.

Step 8 − Click on ‘Create a virtual machine’ and you are ready to use your new machine. It will take a few seconds for the machine to be created.

Create Virtual Machine with Advanced Settings
Step 1 − Choose ‘Custom Create’ instead of ‘Quick Create’ in the options and you will be taken to the following screen.
Step 2 − Choose an image from the list. In this screen, you find that choosing an image is easier based on their category shown on the left side. Let us create a virtual machine for SQL Server for which we have chosen SQL Server on the left side and all the software in this category are shown in the middle. for more info azure certification training
Step 5 − Select the Tier. The size dropdown would change items according to tier. In the basic version, you will get only first 5 options, while in the standard version you will get more options. It should be according to you and you image’s requirements. For example, in this case let’s choose SQL server. It requires minimum A4 machine with 8 cores and 14GB memory.

Step 6 − Enter the username and password and click Next arrow.

Step 7 − Enter DNS name which should be unique as mentioned earlier and select the region.

Under the storage account, it will display the storage accounts that you have already created. As seen in the following screen, an account name is shown in the dropdown which is a storage account created earlier. You can choose an already created account or even use an automatically generated account.

Step 8 − Next is Availability set. This option lets you create a set of virtual machines that will ensure that if a single point fails, it doesn’t affect your machine and keeps the work going on. Let’s choose the option ‘none’ here.
The last option is End Points. End points are used to communicate with virtual machines by other resources you can leave. In a subsequent chapter, we will provide a detailed illustration to configure endpoints.

Step 9 − Click on Next and the virtual machine will be created in a few seconds for you.

Connecting with a Virtual Network
Step 1 − Create a virtual machine using the steps described earlier. If you already have a virtual network created in Azure, it will be diplayed in the highlighted dropdown list as shown in the following screen. You can choose the network as shown in following picture.
Step 2 − When you go to your Virtual Network and management portal created earlier, click on ‘Dashboard’. The virtual machine will be displyed in the resources of that network as shown in the following picture.
Accessing the Virtual Machine
There is a step by step guide on connecting to VM in ‘Compute Module’ chapter earlier in this tutorial. Please refer to it. azure devops training online will help you to learn more effectively

While creating a virtual machine following considerations should be made −

Choose the location according to the user’s location to avoid any latency issues. It is best to choose the region nearest to the physical location of end users.

You must go through the costs that will be incurred based on the size you choose for the virtual machine beforehand, to make sure it is in control.

If you use the already created storage account you will be able to manage things better.
When creating a virtual machine, we come across a part where endpoints can be configured. The two default endpoints enabled while creating a virtual machine are Remote Desktop and PowerShell. What actually is an endpoint? Virtual machine on same cloud can communicate to each other automatically. But in case we need them to communicate with our own computer, we will need an endpoint configured to make it happen. It is basically accessing the virtual machine through a port. An endpoint provides remote access to the services running on virtual machine.
It has a public and private port that needs to be specified while creating an endpoint. Additionally, an endpoint can be accessed securely by activating Access Control Lists (ACL).

In the following section, it is demonstrated how a new endpoint can be configured for virtual machine that’s already been created. However, it can also be done in the same way as creating a new one on configuration part of wizard.

Step 1 − Click on Virtual Machine in your Azure Management portal.

Step 2 − Click on ‘Endpoint’ and then Click on ‘Add’.
In the last chapter, we saw how an endpoint can be created to access a virtual machine; this is quite a tedious task. If a virtual machine in virtual network needs to be connected with on-premise machine, the point-to-site connectivity is needed. Point-to-site connectivity makes it very productive to work with remote virtual machines.

Basically, a machine on-premise is connected to virtual network using point-to-site connectivity. However, we can connect up to 128 on-premise machines to virtual network in Azure. The access to the virtual network in cloud is granted through a certificate. The certificate has to be installed on each local machine that needs to be connected to the virtual network.

Enabling Point-to-Site Connectivity on Existing Virtual Network
If you have already created a virtual network in Azure, you can access it in management portal.

Step 1 − Log in to Azure management portal.

Step 2 − Click on ‘Networks’ in the left panel and select the network you want to work with.

Step 3 − Click on Configure.

Most organizations already have a network on their premises and would want to connect it to Windows Azure rather than putting everything on cloud. It is also called hybrid network connectivity. It is connecting virtual net in Azure to on-premises network. Setting up a site-to-site connectivity network is quite easy for someone who knows the basics of networking like IPs, subnetting and default gateways. azure online courses make you more perfect in the technology.

The things that are required before configuring the network in this case are −

A VPN device that can be configured.
You can configure the VPN device
Site-to-site connectivity is faster than the point-to-site connectivity. It makes transferring of data easier. You just need a shared key to access the network. Unlike point-to-site connectivity, you don’t have to install certificates on each machine you want to connect with the virtual machine. In fact, the same shared key works for each machine.
Externally facing IP address for that VPN device.

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Buddha Community

Microsoft Azure - Deploying Virtual Machines
Aisu  Joesph

Aisu Joesph


Securing Microsoft Active Directory


K-means is one of the simplest unsupervised machine learning algorithms that solve the well-known data clustering problem. Clustering is one of the most common data analysis tasks used to get an intuition about data structure. It is defined as finding the subgroups in the data such that each data points in different clusters are very different. We are trying to find the homogeneous subgroups within the data. Each group’s data points are similarly based on similarity metrics like a Euclidean-based distance or correlation-based distance.

The algorithm can do clustering analysis based on features or samples. We try to find the subcategory of sampling based on attributes or try to find the subcategory of parts based on samples. The practical applications of such a procedure are many: the best use of clustering in amazon and Netflix recommended system, given a medical image of a group of cells, a clustering algorithm could aid in identifying the centers of the cells; looking at the GPS data of a user’s mobile device, their more frequently visited locations within a certain radius can be revealed; for any set of unlabeled observations, clustering helps establish the existence of some structure of data that might indicate that the data is separable.

What is K-Means Clustering?

K-means the clustering algorithm whose primary goal is to group similar elements or data points into a cluster.

K in k-means represents the number of clusters.

A cluster refers to a collection of data points aggregated together because of certain similarities.

K-means clustering is an iterative algorithm that starts with k random numbers used as mean values to define clusters. Data points belong to the group represented by the mean value to which they are closest. This mean value co-ordinates called the centroid.

Iteratively, the mean value of each cluster’s data points is computed, and the new mean values are used to restart the process till the mean stops changing. The disadvantage of k-means is that it a local search procedure and could miss global patterns.

The k initial centroids can be randomly selected. Another approach of determining k is to compute the entire dataset’s mean and add _k _random co-ordinates to it to make k initial points. Another method is to determine the principal component of the data and divide it into _k _equal partitions. The mean of each section can be used as initial centroids.

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Houston  Sipes

Houston Sipes


Microsoft Innovates Its Azure Multi-Cloud, Multi-Edge Hybrid Capabilities

During the recent Ignite virtual conference, Microsoft announced several updates for their Azure multi-cloud and edge hybrid offerings. These updates span from security innovations to new edge capabilities.

From its inception onward, Microsoft Azure has been hybrid by design, providing customers with services that allow ground to cloud and cloud to ground shifts of workloads. Moreover, Microsoft keeps expanding its cloud platform hybrid capabilities to allow customers to run their apps anywhere across on-premises, multi-cloud, and the edge. At Ignite, the public cloud vendor announced several innovations for Azure Arc, Stack, VMWare and Sphere.

At Ignite last year, Microsoft launched Azure Arc, a service allowing enterprises to bring Azure services and management to any infrastructure, including AWS and Google Cloud. This service was an addition to Microsoft’s Azure Hybrid portfolio, which also includes Azure Stack and Edge. Later in 2020, the service received an update with support for Kubernetes. Now Azure Arc has more capabilities with the new Azure Arc enabled data services in preview. Furthermore, the Azure Arc enabled servers are now generally available.

#amazon #microsoft azure #cloud #iaas #kubernetes #iot #edge #google #azure #edge computing #microsoft #hybrid cloud #deployment #aws #containers #devops #architecture & design #development #news

Aisu  Joesph

Aisu Joesph


Azure Series #2: Single Server Deployment (Output)

No organization that is on the growth path or intending to have a more customer base and new entry into the market will restrict its infrastructure and design for one Database option. There are two levels of Database selection

  • a.  **The needs assessment **
  • **b. Selecting the kind of database **
  • c. Selection of Queues for communication
  • d. Selecting the technology player

Options to choose from:

  1. Transactional Databases:
    • Azure selection — Data Factory, Redis, CosmosDB, Azure SQL, Postgres SQL, MySQL, MariaDB, SQL Database, Maria DB, Managed Server
  2. Data warehousing:
    • Azure selection — CosmosDB
    • Delta Lake — Data Brick’s Lakehouse Architecture.
  3. Non-Relational Database:
  4. _- _Azure selection — CosmosDB
  5. Data Lake:
    • Azure Data Lake
    • Delta Lake — Data Bricks.
  6. Big Data and Analytics:
    • Data Bricks
    • Azure — HDInsights, Azure Synapse Analytics, Event Hubs, Data Lake Storage gen1, Azure Data Explorer Clusters, Data Factories, Azure Data Bricks, Analytics Services, Stream Analytics, Website UI, Cognitive Search, PowerBI, Queries, Reports.
  7. Machine Learning:
    • Azure — Azure Synapse Analytics, Machine Learning, Genomics accounts, Bot Services, Machine Learning Studio, Cognitive Services, Bonsai.

Key Data platform services would like to highlight

  • 1. Azure Data Factory (ADF)
  • 2. Azure Synapse Analytics
  • 3. Azure Stream Analytics
  • 4. Azure Databricks
  • 5. Azure Cognitive Services
  • 6. Azure Data Lake Storage
  • 7. Azure HDInsight
  • 8. Azure CosmosDB
  • 9. Azure SQL Database

#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series

Layla  Gerhold

Layla Gerhold


Azure Machine Learning Service

In a series of blog posts, I am planning to write down my experiences of training, deploying and managing models and running pipelines with Azure Machine Learning Service. This is part-1 where I will be walking you through the creation of workspace in Azure ML service

About Azure Machine Learning Service

Azure Machine Learning Service is a cloud based platform from Microsoft to train, deploy, automate, manage and track ML models. It has a facility to build models by using drag-drop components in Designer along with traditional code based model building. Azure ML service makes our job very ease in maintaining developed models and also helps in hassle free deployment of models in lower(QA, Unit) and higher(Prod) environments as APIs. It is integrated with various components in Azure like Azure Kubernetes Services, **Azure Databricks, Azure Monitor, Azure Storage accounts, Azure Pipelines, MLFlow, Kubeflow **to carry out various activities which will be discussed in upcoming posts.

Why Azure Machine Learning Service

In the process of building models, one need to play around with various hyperparameters and use various techniques. Also one need to scale out the resources for training the model if the dataset is huge. Bringing your model development and deployment to cloud makes your job easy. In particular Azure Machine Learning Service has below advantages.

  1. Simplifies model management
  2. Automated machine learning simplifies model building
  3. Scales out training to GPU cluster or CPU cluster or Azure Databricks whenever needed with inbuilt integration
  4. Deployment of models to production with Azure Kubernetes Service or Azure IOT edge is very simple.

#microsoft-azure #cloud-machine-learning #deep-learning #machine-learning #azure-machine-learning

What is Microsoft Azure?

It’s one of the leaders in the cloud computing space, but what is Azure cloud and what is it used for? This ACG Fundamentals episode will give you a high-level overview of Microsoft Azure cloud, so you can understand this cloud computing platform’s strengths and weaknesses, use cases, market share and competition, and how the Azure services all work together.

Introduction (0:00)
Azure Infrastructure (1:07)
Azure Competitors (3:43)
Azure Strengths and Weaknesses (4:18)
Azure Use Cases (6:12)
What’s Next? (7:39)

#microsoft azure #azure #what is microsoft azure?