"The cloud" ☁️ refers to servers that are accessed over the Internet, and the software and databases that run on those servers.
For businesses, switching to 𝐂𝐥𝐨𝐮𝐝 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 removes some IT costs and they no longer need to update and maintain their own servers, as the cloud vendor they are using will do that.
➽ 𝐂𝐥𝐨𝐮𝐝 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 is the delivery of computing services like Servers, Storage, Databases, Networking, including tools and software over the Internet. You usually only pay for the cloud resources you use.
➽ Microsoft Azure is one of the leading cloud computing platforms with solutions including IaaS, PaaS, and SaaS that can be used for 📊 analytics, 🖥️ virtual computing, storage, networking, and much more.
➽ Advantages of Azure?
It offers ⬆️ high-performance and low latency services which are very cost-effective💰.
➽ Azure is the only consistent hybrid cloud, has more regions than any cloud provider, delivers unparalleled developer productivity, and offers more comprehensive compliance coverage.
➽ Want to get certified?
📃Azure Certifications is essential to have 📃 Azure certification while seeking a career in the ☁️ the cloud domain. You will get better 💼 Job Opportunities & 🤑 Higher salaries after the certification.
𝐖𝐡𝐞𝐫𝐞 𝐡𝐞 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐬 :
00:00 = Introduction
00:21 = Agenda
03:00 = Cloud computing overview
06:22 = Scalability vs Elasticity
07:32 = Bring your own license: BYOL
09:33 = Cloud service model : IaaS, PaaS, SaaS
16:33 = Cloud Deployment models: Public, Private, Hybrid
21:53 = Account,Subscriptions & Billing
28:03 = Compute service: VM, App service, Functions, Logic Apps, Container, Kubernetes
01:00:25 = Network Service: VNet, Subnet, VPN Getaway, Express Route, NSG, Bastion Host
01:08:04 = Storage Service: Storage Account, Blob, File, Disk, Queue
01:13:00 = Database Service Azure SQL,Cosmos, Synaps, Azure Data factory, Azure Data Lake
01:19:54 = Data Protection: VM Backups, Azure site Recovery
01:24:25 = Monitoring Service: Monitor, Alerts, Log Analytics
01:33:43 = VM Migration Service: VMware & Hyper-V
01:36:01 = Data Transfer Service
01:38:05 = Data Box & Import/Export
01:41:10 = Azure DevOps: Boards, Repos, Pipelines, Test Plans, Azure Artifacts
01:48:24 = Al & ML on Azure for Data Scientists
01:58:21 = Global Infrastructure: Region, paired Region, Availability Zone, Update & Fault Domain
02:07:03 = Resource, ARM, Resource & Management Group
02:13:08 = Management Tools: portal, Cloud Shell, CLI, power Shell, ARM Templates
02:24:36 = Lab: Create Azure Cloud Account
02:40:02 = Azure Road-map
02:41:54 = FREE Class
02:42:14 = Registration link for FREE Class
Hello guys, Cloud computing is an in-demand skill and essential for Software developers, but with clouds also comes doubt. Among, AWS, Google Cloud, and Microsoft Azure, which one should you learn first?
Does learning Azure make sense given AWS is the most popular public cloud platform? Well, I think learning the Azure cloud makes a lot of sense especially if you are looking for a job in the technical and financial world as Microsoft has the largest market share in the corporate world.
Microsoft has done a great job with their cloud services; they have successfully created services such as testing, deploying, building, and managing applications through data centers.
Microsoft is changing different elements in terms of cloud computing with all these data centers and high-speed internet, we are exploring new boundaries. It’s uncanny how things are progressing, but we need to keep with new technology.
If you want to learn Microsoft Azure concepts and services and looking for free online training courses and classes then you have come to the right place. In the past, I have shared free and paid courses to learn Microsfot Azure, AWS and Google Cloud Platform and today, I am going to share free courses to learn the Microsoft Azure Platform.
We have sorted out and handpicked the best and free Microsoft Azure online courses from places like Udemy, Youtube, and Pluralsight. These courses are going to provide you with great insight into Microsoft Azure Cloud services and functioning.
Each course has focused on a certain area of learning, so it’s utmost important that you take a look at all those courses personally as well. We will be providing you with a short description of the course that can provide you with the summary, of course, their content and the vision behind them.
By the way, if you don’t mind paying few bucks for learning a useful skill like the Microsoft Azure platform and looking for more comprehensive and in-depth courses to learn Azure services then I also suggest you check out AZ-300/AZ-303 Azure Architecture Technologies Exam Prep 2020 course by Scott Duffy on Udemy. It’s one of the most comprehensive courses to learn the Azure Cloud platform.
#cloud-computing #microsoft-azure #azure #course #machine-learning
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
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.
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.
#microsoft-azure #cloud-machine-learning #deep-learning #machine-learning #azure-machine-learning
When it comes to research in new-age technologies, Microsoft has been striving hard to stay ahead of its competitors. From recommendations to gaming, the tech giant has been using popular techniques like reinforcement learning to create efficient products for customers that match their interests.
The foundational work in reinforcement learning (RL) started back in 1992, in which the researchers worked on Simple Statistical Gradient. This year, the tech giant has made significant contributions in the ongoing AI conference known as NeurIPS 2020. The three key research areas that are being focussed this year include batch reinforcement learning; a strategic exploration that has given rich observations; and representation learning.
#microsoft #microsoft research #microsoft research lab #reinforcement learning #reinforcement learning environment #reinforcement learning systems
Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.
In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.
Pros of Machine learning
AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.
**Do you know the Applications of Machine Learning? **
With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.
As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.
AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence
You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.
**Cons of Machine Learning **
With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:
1.** Information Acquisition**
AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.
ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
Likewise, see the eventual fate of Machine Learning **
Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.
AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.
Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.
#machine learning online training #machine learning online course #machine learning course #machine learning certification course #machine learning training
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.
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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