How Microsoft Azure Is Accelerating Hardware innovation

👉This year, we are excited to showcase some of our projects and technology at OCP Global Summit and share our learnings on the path of building a more reliable, trusted, and sustainable cloud alongside industry partners and the open source hardware ecosystem.

⭐️Thank you for your interest in the blog, if you find it interesting, please give me a like, comment and share for everyone to know. Thanks! ❤️️

#azure #Microsoft

What is GEEK

Buddha Community

How Microsoft Azure Is Accelerating Hardware innovation

Aisu Joesph

1624327316

Securing Microsoft Active Directory

Clustering

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.

#ad #microsoft #microsoft-azure #azure #azure-functions #azure-security

Cathryn Windler

1626901920

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?

Houston Sipes

1602315003

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

Fanny Considine

1636732800