Deion  Hilpert

Deion Hilpert

1593277860

Microsoft partners with OpenAI to create Azure supercomputer

Microsoft has partnered with OpenAI to create an Azure-hosted supercomputer for testing large-scale models.The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs.

#ai news #microsoft partners with openai to create azure supercomputer #openai #azure

What is GEEK

Buddha Community

Microsoft partners with OpenAI to create Azure supercomputer
Deion  Hilpert

Deion Hilpert

1593277860

Microsoft partners with OpenAI to create Azure supercomputer

Microsoft has partnered with OpenAI to create an Azure-hosted supercomputer for testing large-scale models.The supercomputer will deliver eye-watering amounts of power from its 285,000 CPU cores and 10,000 GPUs.

#ai news #microsoft partners with openai to create azure supercomputer #openai #azure

Aisu  Joesph

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

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?

Layne  Fadel

Layne Fadel

1623894514

How to become a Microsoft Learn Student Ambassador (MLSA)

doesn’t matter if you are a fresher or a final year bachelor’s student or a master’s student, you can apply ! All you need is passion for technology.

By enrolling in Microsoft student ambassadors program (earlier Microsoft student partner), you can -

  1. Upskill yourself by Microsoft courses, get mentored by Microsoft professionals, earn badges and certificates.
  2. Host events, workshops and hackathons at your college, teach people how to code and become a LEADER.
  3. Network with global community.

I got to know about this program last year (2020) in early may and by 12th of may, I completed and submitted my application. I got selection mail on 7th August.

Right now, I am in my final year of engineering.

PERKS

  • Microsoft swags and goodies
  • USD150 monthly Azure credits
  • Office 365, Visual Studio Enterprise, Techsmith Snagit and Camtasia softwares for free.
  • MTC certification exam vouchers and LinkedIn learning free for 6 months.
  • Mentorship and networking with Microsoft professionals.
  • and much more !

Excited ? Apply now !

STEPS TO FOLLOW

  1. Visit their official website, click on ‘Apply Now’ and Sign up.
  2. Application form consists of basic information, written sample, technology skills, resume and a short video introduction.
  3. Make a video explaining your passion for technology and why you want to join the program. Upload it on YouTube (or Google drive).
  4. [My video for reference]
  5. Fill the form and submit.

KEEP IN MIND

  • You can fill the application form throughout the year. New students are accepted into the program quarterly.
  • After submitting, you can edit your application until it goes to the review state by Microsoft. You’ll learn about the deadline date while filling the application form.
  • Do not skip filming the video; it increases your chance of getting selected !

#microsoft-azure #microsoft #microsoft-student-partner #mlsa #microsoft-ambassador

Aisu  Joesph

Aisu Joesph

1626490533

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