Plesk integrated Windows Cloud Servers

February 10, 2021

What are Plesk integrated Windows Cloud Servers?

Since its foundation back in 2000, Plesk has grown up to become one of the most used web cloud computing control panel software. This innovative control panel, available for Windows, makes it seamless to manage and automate the content on the most used content management platform: WordPress. It is being currently used to automate 12M+ websites and 16.5M+ mailboxes across 140 countries.

When the management of Plesk is merged with the revolutionary cloud servers, we get what we call the Plesk integrated cloud servers. With E2E Networks’ Plesk integrated cloud servers, you can get started with Content Management Systems (CMS) within a few minutes. That’s because we have made CMS simple for you. 

What are Plesk Integrated Windows Cloud Servers?

As mentioned, Plesk integrated Windows cloud servers are cloud servers equipped with all the features of Plesk with Windows OS. They let you launch a Plesk server within a few clicks and get you started with efficiently controlling your content across multiple CMS platforms.

The best part is that you can leverage all the Plesk features with cloud servers’ benefits, including enhanced security, centralized collaboration, improved scalability, redundant server environment, and optimal pricing structure. Hence, it brings you the best of both Plesk and cloud computing so that you can quickly scale your business seamlessly.

Plesk integrated Windows cloud servers further enhance the security of your cloud-based websites and web applications with various tools and applications. For instance, Plesk supports tools such as SSL certificate, backup manager (to automate backup), firewall management, and DDOS protection. The blend of Plesk and cloud servers lets you create highly cost-effective innovations. It enables you to work flexibly to get the maximum productivity output. Also, you have the scalability feature that allows you to deploy single or multiple complex sites and applications all within the same time frame.

E2E Networks’ Windows cloud servers come with pre-installed Plesk with all the licenses of Windows OS, making it easier for you to manage nodes, instances, and machines using an interactive Graphic User Interface. Hence, Plesk integrated Windows cloud servers become the best for WordPress multisite cloud computing

Its simple UI and ease of navigation make it equally beneficial for everyone, making it a must-have Windows server cloud computing control panel.

Who’s Plesk Integrated Windows Cloud Servers for?

Plesk and Plesk integrated Windows cloud servers are for every web professionals, including IT admins, content managers, developers, and digital agencies.

  • IT admins

You can manage and automate websites, bandwidth usage, emails, and accounts from a single and simple dashboard. Plesk integrated Windows cloud servers are compatible with several web servers. The feature that will excite you the most as an IT admin is that they provide optimal security.

With tools and technologies such as Firewall management, Rootkit detection, Fail2ban, and DDOS protection, Plesk integrated Windows cloud servers are best for Windows Cloud servers. Not only that, they also give you control by allowing you to provide role-based access, making it the first choice for WordPress deployment.

  • Developers

Plesk integrated cloud servers bring you an integrated platform with full-fledged development and deployment platform. They allow you to alter, deploy, and run changes without any hassle right on Windows or WordPress cloud.

The integrated coding framework lets you add, alter, or delete code and access the database on the go. It also gives you access to numerous other features, including SEO tools, CMS, analytics tools, and marketing services on a single tap.

  • Content managers

You can choose any CMS platform: WordPress, Drupal, or Joomla based on your preference as Plesk provides support to all. Plesk allows you to manage and automate multiple instances from a single platform. It also allows you to secure your CMS in a single click.

With the most preferred CMS platform, WordPress, Plesk integrated cloud servers provide you with additional benefits as they come with an Ultimate WordPress Toolkit. The Toolkit enables you to mass-manage all the sites, instances, themes, and plugins effortlessly. It also lets you clone your sites and then make changes so that you don’t have to mess with your live website and always have a backup. Hence, with Plesk integrated cloud servers, E2E Networks is the best WordPress cloud server option you have at your disposal.

  • Digital agencies

Plesk integrated cloud servers offer a single stop solution for digital agencies. They provide you with a single view dashboard that lets you get a master view across all your Joomla, Drupal, and WordPress projects. 

It offers features such as managing multiple servers from a single place, offering value-added services, providing role-based permissions, an advanced development feature set, and a robust CMS toolset. Plesk and Plesk integrated cloud servers with such features make it a win-win for everyone within a digital agency.

What are the Features of Plesk?

Plesk brings you tons of general features, but what makes it stand out from other control panel software is that it also provides specialized components for IT administrators, developers, and digital agencies.

Plesk General Features for Everyone

  • Hundreds of extensions: Plesk offers hundreds of APIs, plugins, and other attachments to access internal and external features across multiple CMS platforms and repositories such as GitHub and Bitbucket.
  • Domain: Easily add multiple domains and subdomains, and rearrange them among all your subscriptions. Also, secure them with SSL certificates in a click.
  • Email: Webmail support for setting up automated email notifications and responses. Plesk also allows auto-detection of clients on Outlook and Thunderbird.
  • Database: MariaDB database support for easy management of data on customer sites.
  • Files: Upload multiple files simultaneously and search among the uploaded files with the improved file manager.
  • Application catalog: Supports multiple third-party applications.
  • Backup: Schedule and automate the backup process to prevent losing any data.
  • Mobile apps: The pocket-friendly version of Plesk allows you to manage your business on the go.
  • Website builder: Supports multiple site builders so that you have the flexibility to create a fully responsive website.
  • Multi-language support: It is available in 32 languages.
  • Self-repair tool: With advanced monitoring capability and self-repair tool, Plesk can handle minor technical problems without manual support.

Apart from these general features, Plesk offers several exclusive features beneficial for professionals, including IT administrators, developers, and digital agencies.

Plesk Features for IT Administrators

  • User accounts: Lets you create accounts and set different credentials for individual users.
  • User roles: Grant role-based access to various users for different features and functionality.
  • Subscriptions: Create and manage multiple subscriptions efficiently.
  • Service plans: Choose from multiple service plans based on disk space and bandwidth requirements.
  • Mail server support: Pre-installed mail server support for both Linux and Windows.
  • DKIM, SPF, and DMARC protection: Prevent phishing attacks with standard email protection.
  • Database management: Manage even vast databases seamlessly.
  • System resource usage limit: Limit the amount of usage by an individual subscription.
  • PCI DSS compliance: Get PCI DSS compliance for Linux servers.
  • Task Scheduling: Schedule various tasks on the go.
  • System updates: Update packages manually or set them to be updated automatically.
  • Plesk migrator: Migrate to different panels, including cPanel, DirectAdmin, Confixx, and more.

Plesk Features for Developers

  • Ready to code environment: Supports multiple programming languages like PHP, .NET, Javascript, Python, Pearl, Node.js, Ruby, and more.
  • NGINX: Scale-up PHP processes using NGINX. Also, manage how different websites use cache to enhance the performance of your WordPress sites.
  • Git integration: Integrate Git repository to push and deploy your websites from local or remote repositories.
  • HTTP/2 support: Speed up the data pocket communication over the network with HTTP/2 support.
  • Docker support: Access, alter and push Docker containers to quickly deploy them in your code.
  • Command-line interface: Use the command line to monitor, manage, and use Plesk objects to perform different operations.
  • XML API: XML API lets you interact with third-party applications.
  • SDK: Unique Software Development Kit (SDK) to customize your coding environment.
  • Cloud backup: Backup unfinished code to your preferred cloud server.
  • Event handlers: Efficiently handle events and set up actions to perform when they are triggered.

Plesk Features for Digital Agencies and Web Designers

  • Developer dashboard: Plesk gives you a single-view dashboard to monitor and manage all your projects across different CMS platforms such as WordPress, Joomla, and Drupal.
  • Server health monitor: Ensure that all your sites are running smoothly without any interference with the help of a server health monitor. It also lets you keep track of resource usage by a particular website.
  • Reports: You can generate and export resource usage reports for all your sites. Analyze the reports to make informed decisions.
  • Easy monitoring: Show error messages and warnings from the Plesk interface itself.
  • WordPress toolkit: It is an all-in-one toolkit for efficient management of all your sites running on WordPress.
  • Joomla toolkit: It is an all-in-one toolkit for efficient management of all your sites running on Joomla.
  • Branding: This allows you to easily customize the UI on any website and give your unique touch to the UI to enhance your brand.
  • Developer environment: Ready-to-code environment to alter the code and manage small glitches and bugs found in the code.

These are some of the features you get with Plesk. Now, imagine all these features, along with the benefits of cloud computing. That’s what Plesk integrated Windows cloud servers bring to you.

Why Choose E2E Networks’ Plesk Integrated Windows Cloud Servers?

While the features of Plesk integrated cloud servers are tempting enough, the additional benefits of E2E Networks make it the best option for Windows cloud servers. The Plesk integrated cloud servers on E2E Networks’ clouds provides other benefits such as:

  • Latest hardware: Our cloud servers are powered by Intel Xeon processors, which are x86 microprocessors suited for servers. 
  • High reliability: Our Plesk offerings are backed up by a massively 99.9% uptime Service Level Agreement (SLA), ensuring in-time services.
  • Best Latency: Our data centers offer a very high-bandwidth and low latency for seamless network communications.
  • Full Feature: E2E Networks’ Plesk integrated cloud servers use Plesk Onyx, the latest software version. Since it is the newest version, it is equipped with all the software features, including easy setup and optimal security.
  • Content management: Pre-installed Plesk in our Windows cloud servers, allows you to deploy content within a few minutes. Also, the cloud development framework for Plesk makes it easier to code, develop, and deploy content across all CMS platforms.
  • Access: We allow you to take control of everything. With our Windows VPS cloud computing, you get full root access along with all the administrative privileges on the server itself. Our Windows virtual cloud server allows you to run your own cloud copy, thereby giving you super-user level root access.

All these features and additional advantages make us the best Windows cloud provider. With all these advantages to leverage, you will handle all your resources, applications, and sites much more efficiently.

Our Plesk integrated cloud servers’ pricing system is broadly categorized into two groups: one with 30 domains and unlimited domains. You can also select from multiple plans and compare the clouds to choose the optimal pricing for yourself. Hence, apart from providing the best Windows cloud servers, we also offer everyone an affordable price range.

If you have any other queries, refer to our E2E’s Plesk FAQ page for help and assistance.

We provide Plesk integrated Windows cloud servers at minimal hourly rates, giving you a Windows cloud server option that provides value for money. You can leverage E2E Networks’ Windows cloud servers to run your mission-critical applications on the cloud servers through Plesk innovative control panel.

Apart from the Plesk integrated servers, we also provided services for top Windows cloud servers and other cloud computing services. You can leverage a high memory cloud server, cPanel server, and NVIDIA GPU cloud server, among others.

Latest Blogs
This is a decorative image for: What is SOTA in Artificial Intelligence?
August 5, 2022

What is SOTA in Artificial Intelligence?

If you are one of those people who love to pursue Artificial Intelligence and related operations like Machine Learning, then you must have certainly come across a term called SOTA. It is one of the much-talked things in the field of AI and holds a lot of gravity.

But for those who are interested yet are clueless about what SOTA is and what its relevance is in the field of AI, here is a simple definition of SOTA, what it means, and what importance it holds.  

What is SOTA?

SOTA is an acronym for State-Of-The-Art. In the context of Artificial Intelligence (AI), it refers to the best models that can be used for achieving the results in a task. Mind you; it should be an AI-specific task only. SOTA models can be applied in many ways in AI. It could either be applied to –

(a) Machine Learning (ML) tasks

(b) Deep Neural Networks (DNNs) tasks

(c) Natural Language Processing (NLPs) [this is a subset of deep neural networks]

(d) Generic tasks

How does SOTA help in AI?

Using SOTA models in AI has many benefits of its own. The primary benefits are –

  • Increases task precision

First of all, you should check which parameters define your SOTA Model. These parameters could be the recall or the precision, or the area under the curve (AUC). It could be any metric you choose. After that, you could determine the value of the SOTA for each of the chosen metrics. If these metrics get a high score (about 90%-95%) in performance accuracy, then it is labelled as a SOTA. Now it is pretty obvious that these models score high on accuracy, so the AI task will be as close to what the users need to do.  

  • Increases reliability

Since the precision of the SOTA models is high, as mentioned above, the reliability of the AI task also increases. If it is a machine learning task or deep neural network task, then be assured that the results are pretty much what they are supposed to be. They can be trusted and not be considered a random test of sorts. But how do you know that the SOTA is trustworthy?

So, here’s a suggestion. While you are building the SOTA test, it would be better if you ran noise experiments on the SOTA model. It will help you in measuring the standard deviation in the many identical tests runs that you are subjecting the model to. You can use this measurable deviation as a sort of shift or tolerance, and then you can compare the original SOTA result and the reproduced result. Testing the results will help you in verifying the features that are required in the algorithm in the future.

  • Ensures reproducibility

If you want your AI product to be agile and lean, then you will be able to ship the minimal viable product (MVP or a minimal version of your envisioned product) quickly to all your customers. You can then proceed to get user feedback and improve iteratively. Therefore, reproducibility in your SOTA model can be considered to be a good practice. This will help you in making compromises in your algorithm. You can also ship your algorithm quickly. And yes, about the customer feedback you have collected, you can use it as a guide for all your efforts in future product improvements.

  • Reduces generation time

Since the SOTA model helps you in reproducibility of the algorithm or the product, it also helps you in saving time when you put the entire process on the conveyor belt. That means you can make a saleable product from a prototype in less time than when you made the same product from scratch. All you need is to reproduce the algorithm on the parameters on which it needs to be tested are already in possession, so yes, you save a lot of time in the generation of the product.

When should you run a SOTA test?

You should run SOTA tests as frequently as possible. Frequent SOTA tests are a rule of thumb in AI. But it is advisable to run them once a week. You should also run the SOTA tests when you are incorporating important changes. It is advisable to run the SOTA tests should be run on a cloud virtual machine using a good pipeline like Jenkins.

Where can the SOTA models be used?

SOTA models are used in various artificial intelligence activities –

(a) Object detection by deep neural networks

(b) Single shot multi box detectors

(c) Self-adaptive tasks like choosing variable patterns

This list is not exhaustive as the possibility of using SOTA encompasses many branches of AI. Be on the lookout for future blogs to know more about SOTA and its applications in every subset of AI.

To sum up, SOTA models have played a crucial role in advancing AI and ML technologies. It has introduced structural efficiency that has boosted performance. Now, developers run various SOTA tests using the virtual GPUs, which further streamlines the process and reduce the upfront infrastructure costs, and E2E Networks is making it possible with cloud GPUs.

Reference Links

https://towardsdatascience.com/software-design-patterns-and-principles-for-a-i-1-sota-tests-3dd265c6bf97

https://deci.ai/blog/sota-dnns-overview/

https://paperswithcode.com/sota

This is a decorative image for : Should you migrate to E2E Cloud from Digital Ocean?
August 5, 2022

Should you migrate to E2E Cloud from Digital Ocean?

There comes a time in professional business life when they want to migrate all their data, resources, applications, workloads, etc., to the cloud for security reasons. It is a process of transferring data from on-premises to the cloud. Everyone prefers to use the cloud these days, but cloud migration can be an overwhelming process. Business wants to go with a service with minimal downtime and a hassle-free experience. So if you are using Digital Ocean for a while and now prefer to switch to another service, then this one question must have popped into your mind: should you migrate to the E2E cloud from the digital ocean? Here in this blog, we are going to answer the same. But first thing first, let's understand the benefits of migrating to the cloud.

The Top Benefit of Migrating to the Cloud

Businesses prefer to rely on cloud platforms due to various reasons, some of which are listed below:

1. Security

The first benefit of using a cloud platform is the high level of security compared to other network systems. The shared responsibility model is used in the cloud system, which is why this model is more successful than the traditional network system. All the data and resources of the business are stored centrally, which makes the cloud network convenient.

2. Scalability

The second benefit of using cloud platforms is scalability which means businesses can increase and decrease their requirement anytime based on the need and performance of the company. Firms and organisations have the flexibility to alter their infrastructure needs and workloads based on the current condition.

3. Integration

Another benefit of switching to a cloud platform is seamless integration. Businesses can connect multiple systems altogether without any difficulty. Not only does this increase the efficiency of the company, but it also saves money. Cloud services are updated and improved regularly, so the chances of decreased efficiency are less.

4. Cost

Lastly, one of the significant benefits of using cloud networks is cost. It reduced operational costs. Business here only pays according to usage, saving a lot of money.

Should You Migrate to the E2E Cloud From Digital Ocean?

Yes, that is possible, and with the recent hike in Digital Ocean's price, the only convenient option for organizations is to migrate to the best affordable solution. And when we talk about affordability, the E2E cloud seems to be the best in the market. The answer has a high-quality infrastructure. Around ten thousand clients are relying on E2E cloud solutions. The platform is built to fulfil the need of every kind of business. The solution is designed to execute real-world use cases such as NLP, health tech and consumer tech.

The thing which is loved by businesses is the quick deployment process, and the E2E cloud understands that very well. That's why companies will get the one-click deployment at their fingertip. And most importantly, the pricing model business will bring to the E2E cloud is unbeatable.

The process of cloud migration can be excruciating. Newbies and newcomers can't do it without proper assistance. That's why the E2E cloud is readily available at their customer service with the required help.

Sign up to E2E Cloud Now

With all that in mind, if you are looking for the most convenient solution, then give the E2E cloud a try. Not only will we help you save money, but you will get the cloud platform with high reliability too. Reach out to us to get a consultation on the migration process from Digital Ocean to our forum.

References

https://touchstonesecurity.com/cloud-migration-benefits/

https://www.vultr.com/news/Should-you-switch-from-DigitalOcean-to-Vultr/

This is a decorative image for- How do data scientists use PyTorch?
August 4, 2022

How do data scientists use PyTorch?

PyTorch was introduced for the first time in 2016 and it is a deep learning open-source framework. It has become very popular among developers due to its ease of usage and efficiency. PyTorch is getting huge critical acclaim because of its compatibility with a high-level programming language Python which is also favored by data scientists and machine learning developers.

About PyTorch

Deep learning models are a type of machine learning model that have multiple applications and usage which include language processing, image recognition, and more. PyTorch is an elegant framework that can help in the construction of deep learning models. This framework has been written using Python and the best part about PyTorch is that it is extremely easy to learn and implement for machine learning developers.

Furthermore, PyTorch is unique in its support of GPUs. Other exclusive features of PyTorch include auto-differentiation, reverse-mode, computational graph, etc. This is also why PyTorch is a popular choice among developers for prototyping and fast experimentation.

Why is PyTorch a popular choice among developers and data scientists?

PyTorch is the product of Meta’s Artificial Intelligence research lab and others. The framework has incorporated the Python programming language in the front end with a resilient and productive backend library from Torch which is also GPU accelerated. The entire framework concentrates on unreadable code, quick prototyping, and assisting multiple categories of deep learning models. 

Although PyTorch enables the friendly yet authoritative programming approach for data scientists and developers, simultaneously providing production graphs. The framework was released as open source in the year 2017 and because of its Python roots, it has become fairly popular among machine learning programmers.

Benefits of PyTorch for data scientists

Due to its innovative characteristics, PyTorch is extremely popular in deep learning. For example, PyTorch has implemented a chainer technology known as reverse-mode automatic differentiation. To put it more simply, the method is like a tape recorder that completes each and every operation, then computes the gradients, and finally iterates the entire process. 

Due to this particular feature, debugging in PyTorch is very simple and it can also adapt to specific applications such as dynamic neural networks.  PyTorch is also well accepted for prototyping because every repetition can provide different results.

Python developers extensively use PyTorch which has been developed using the Python language. The framework utilizes the define-by-run eager execution mode and authoritativeness of the language through which all the operations are executed. 

Although Python is fairly popular among developers and other programming languages, a recent survey by Datanami shows there has been a growing focus on machine learning, deep learning, and AI thus paving the way for industry-wide PyTorch implementation.

For existing Python developers and data scientists, PyTorch has become a good choice for its futuristic scope. Moreover, those who are comparatively new to deep learning can already come across an enlarging library of deep learning courses which are specifically based on PyTorch. Since its release, the API of this framework has remained consistent and that is why PyTorch is significantly easy to decipher for experienced Python programmers.

If we look at any particular strength of PyTorch then it is prototyping in smaller projects. It is also beneficial for academics and research communities because of its ease of usage and flexibility. Facebook’s AI research lab is also working tirelessly to ameliorate the productive application of PyTorch.

The latest releases of PyTorch have included multiple enhancements. Moreover, it has also added ONNX, or Open Neural Network Exchange which can help the developers comply with the deep learning models that will be productive for their projects or applications.

Features of PyTorch

Here is a list of important features of PyTorch:

 

  • PyTorch has an excellent and active community of developers that provides brilliant tutorials and documentation. You can visit their forum at PyTorch.org.
  • The entire framework has been developed using the popular programming language Python and the developers have also included Python libraries such as NumPy to conduct scientific computing. For the compilation of Python to C and to provide a better performance, SciPy and Cython have been used. 
  • PyTorch is very easy for data scientists and Python developers because it has similar syntax and utilization.
  • Major cloud platform supports PyTorch.
  • The scripting language of PyTorch is known as TorchScript and it is very easy to use as well as ductile when used in eager mode (eager mode is a specific mode of this framework where operations are executed instantly as they are derived from Python). You can also change to the graph mode if you require better optimization and more speed in C++ runtime settings.
  • PyTorch can effectively support parallel processing, GPU, distributed training, and CPU, which means any computational work can be allocated among various GPU and CPU cores. Furthermore, you can also conduct training on multiple machines using multiple GPUs.
  • Dynamic computational graphs are supported by PyTorch which enables the network behavior to be transitioned during the runtime. This flexible characteristic is a major feature that sets apart PyTorch from the existing deep learning frameworks (because the rest of them require neural networks to be delineated as a static object before runtime.)
  • PyTorch also has a storage of pre-trained models that can be replicated using a single code line.
  • PyTorch as a deep learning framework has both the eager mode (for experiments) and graph mode (for the execution of performance).
  • You can extend the core functionality of your applications using the brilliant APIs of PyTorch.
  • The libraries and tools of PyTorch range from reinforcement learning to computer vision.
  • The pure C++ frontend interface which the python developers are accustomed to is supported by PyTorch and you can also create high-performance C++ applications using the same.
  • In PyTorch, you will be easily able to construct a brand-new custom component as a subclass under the standard Python class.
  • You can easily import the libraries and parameters which can further be efficiently dispensed with the help of TensorBoard (which is an external toolkit.)

Practical use case of PyTorch for data scientists

Due to the PyTorch framework being convenient and flexible, it is being used in multiple projects and applications such as natural language processing, reinforcement learning, image classification, etc. Let us discuss them in brief:

Natural Language Processing (NLP)

If we look at software or virtual assistants, we will be able to understand how machine learning has made significant breakthroughs in understanding natural languages. 

Most of these models utilize a flat sequence of characters or words in the form of recurrent neural networks or RNN to process the sequences. Yet, a lot of linguistics think that language can be comprehended most efficiently if we use a stratified tree of phrases.

That is why a lot of research has been done on the deep learning models which are termed as recursive neural networks that undertake this approach recommended by linguistics. Although these models do have a complex nature and are hard to implement, PyTorch smoothens these difficult natural language processing models to make them much easier and more efficient. Right now, Salesforce is utilizing PyTorch for multi-task learning and NLP.

Computer vision

You can utilize computer neural networks to reinforce the development of image classification, object detection, and generative application. The framework also helps the programmers to process images and videos through which they will be able to construct a detailed and unambiguous computer vision model.

Reinforcement learning

You can easily control the motion of robots, create business development plans and reinforce robotic processes with the help of PyTorch.

How data scientists can work with reinforcement learning with the help of PyTorch

For data scientists, there are multiple use cases of PyTorch in the deep learning field. Moreover, you can experience better results with the implementation of PyTorch in multiple projects regarding style transfer, image classification for identifying fake goods, etc. 

Currently, tech giants are also using PyTorch for natural language processing. If we carefully look at the progress and implementation of PyTorch in the field of deep learning and artificial intelligence, learning this framework as one of your technical abilities can open up lots of future opportunities for you.

Reference links:

https://medium.com/geekculture/how-pytorch-helps-data-scientists-in-reinforcement-learning-a8843e441c1

https://towardsdatascience.com/minimal-pytorch-subset-for-deep-learning-for-data-scientists-8ccbd1ccba6b

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