Guide on How to Use E2E Cloud Servers More Efficiently

January 27, 2021

In an attempt to find access and download files from a server, cloud servers prove as an effective solution where it offers the ability to access the files from any remote location and a gadget. Cloud servers have made its place in every business, as it offers the most suitable solution for online data storage.

Need for Cloud servers

With conventional hard drives, there were many persisting issues like storage capacity limit, more exposure to files getting corrupted, and the offline storage costs. The issues were resolved through the implementation of cloud servers effectively. Cloud servers have made the process of sharing files and resources a secure and hassle-free process. All businesses and IT professional industries are inclined towards having a digital servers space to ensure never losing out on important data. All of this comes with a guarantee that the contents of the servers would be encrypted for maintaining privacy and ensuring safety against any hardware failure.

The global cloud computing market is aimed to cross the $330 Billion mark by the end of 2020

E2E has been a household name in the Indian market providing cloud computing solutions to businesses and SMEs. It has helped provide effective solutions to more than 10,000 customers actively using the platform. E2E has been successful in serving its customers with superior performance at affordable costs with bankable customer service.

What are the Different Types of Cloud servers Offered at E2E?

There is a wide range of cloud solutions offered at E2E, developed by understanding the market scenario and the customer requirements be it GPU cloud server, VPS and windows cloud servers, or any other solution.

  • CPU Intensive Computing C2- Series

This product is a perfect fit for businesses looking to expand their reach and reduce their downtime to a negligible rate with high performance and considerably high server space. Infused with the latest set of Intel processors, the C2 series will ensure heavy data operating applications.

  • C2 series with Red hat enterprise Linux

C2 series offers high performance to compute instances capable of supporting high IOPS and memory for website applications. This helps to compute high computing-based workloads to improvise on performance and without any downtime. The red hat enterprise is of the very recent offerings and top-ranked in the public cloud environments.

  • High Memory Cloud

Offering a cloud disk space of 225 GB SSD, the 2nd generation memory focussed plans are more inclined towards providing sufficient RAM support for the websites to maintain prolonging customer satisfaction.

  • Windows Cloud

Windows cloud hosting is one of the most affordable and opted services. At E2E, we offer numerous ranges of features offered at different price ranges to help you find your most suited solution.

  • Windows SQL Cloud

The SQL cloud is a perfect fit for windows operated systems as they are configured to support database workloads. It helps you adjust to the website’s needs and increase the capacity and the servers to ensure that there is no effect on the website application and the processes operated on it.

  • Plesk Windows Cloud

Plesk is an innovative control panel that helps ease the management process with a Graphic User Interface (GUI). This feature comes pre-installed on the windows cloud servers. It proves as a viable solution to enterprising reseller platforms and online stores to support the applications on Windows.

  • GPU Smart Dedicated

Built with the integration of the Nvidia T4, Tesla V100 and NVIDIA A100, it ensures that the upcoming and improving sectors like machine learning and artificial intelligence would not be limited by hardware support. The GPU based web cloud solution would help configure the visualization process with the combination of unbeatable performance for professional workflow requirements.

  • Linux Smart Dedicated

Offered at the lowest hourly rates, the smart, dedicated cloud servers solution aims to increase the performance of the machine learning processes and eliminate the effects of CPU steal along with customized automation to numerous backups, ensuring the safety and security of your data. The dedicated server offers an interactive API to give the managing ability of the machines via programming code.

  • cPanel Linux Cloud

Web hosting services have increased exponentially due to the rise in demand for online stores and learning management systems. cPanel cloud server comes with full root access and licensing for optimized security and with no additional cost. The customized solutions are offering a security tool to ensure website application safety and a plugin that will help with the onboarding of any other additional applications required on the website.

All the solutions offered by E2E are affordable and stored locally on Indian data centers, configured with high frequency for optimum performance characteristics. All the web hosting and cloud solutions are configured towards the optimization and smooth working of plugins and high data-based applications.

After understanding the types of solutions offered, the important aspect revolves around choosing the right cloud servers solution for your needs.

  • Look for plans that go beyond servers and offer excellent customer support

Cloud servers solutions though easy to operate, but on the back end, require extensive support from the service providers to ensure that the business owners are not affected in any way.

E2E ensures that the customers are justified for their services and providing them a great experience overall.

We help you understand and offer the best suitable plans for your needs with a personal touch to ensure that the user experience does not arise any conflict and look for solutions that are more focused on online and offline support.

  • Sync data across multiple devices

Secure servers or server windows VPS, all solutions are provided by E2E as cloud servers eliminate the need for syncing devices. All your data is being uploaded on the server mainframe, giving them the freedom to access the server from any device.

E2E cloud computing systems also facilitate the use of real-time collaboration that helps multiple users make changes for a file simultaneously. This is a great solution for project management and multitasking scenarios.

Close to 90% of the IT-based organizations have their database online on the cloud today, and more than 30% of the revenue stream of such companies are spent towards cloud handling and management services.

  • Using tools that facilitate cloud servers for better accessibility

Cloud-based applications have registered a staggering growth of 46%

The applications, when shared and installed on online servers, provide instant access to the data, which can be retrieved from any local connection or a mobile device. It comes with an easy-to-use user interface that helps make this process simplistic and features the ability to drag and drop files to be saved to the server's drive.

  • Keep data encrypted

Encrypted data ensures that even if data is misplaced or tampered with, it is useless and almost impossible to break into. The encryption process involves encoding the data before being uploaded to the server, which can only be accessed by the authorized user from an encrypted connection. The need for encryption equipped products is quite serious. The cloud encryption market alone is worth $2401 million by 2022.

  • Conduct performance analysis and implement continuous monitoring

With constant performance evaluation of your servers plan, it helps to keep track of all the attributes that the plan was designed to offer. It shows the areas requiring attention and improvement statistics to increase the server’s response time and overall quality.

  • Make sure to evaluate additional features and use auto-scaling

While taking up on a server's plan, make sure to have an understanding of all the features and functionalities for better perception. E2E helps potential customers by explaining each feature on a deeper level and connecting with their products.

With the help of auto-scaling, users can identify the amount and capacity of the computational resources that are measured in terms of server status. It helps to identify the traffic scenario on the web page application and ensures that the cloud can handle the current traffic demand.

  • Opt for automation-based control systems

Cloud servers work like clockwork. It finishes the ongoing job at hand and then carries out the other uploading processes. There are times when multiple users are accessing the servers platform. This increases the time.

The data optimization and restructuring methods are implemented in automation to make proper use of the servers space and services.

  • Retention period of data and value criteria

There are different policies implemented on different plans that provide a retention period of one week to one year. Before subscribing to a plan make sure to go through the server's retention period.

Define the duration time as per requirement and replace the existing policy to ensure you have the full benefit of the cloud service.

Windows cloud hosting or cloud servers have their servers API console, and so does E2E provide support on meeting the compliance standards and save your time.

  • Optimize cost and expenditures

Cloud servers are used to optimize costs and expenditures incurred to sustain the website and web page applications. If the current servers plan is not sufficient, the service plan should always cater to the possibility of modifying the services offered.

Comparing SSD-based Cloud Servers with HDD-based Cloud Server

SSD-based servers can read sequential data up to 550 Mbps, whereas HDD can perform at 125 Mbps in terms of writing speed.

SSD has been outperforming conventional hard drives as they offer high reliability, less and effective power usage with proper streamlining of processes. SSD does not cater to dropping variable seek time or latency issues, as HDD slots require to write NAND cells, their writing speeds are increased and consumes high power.


The aging process is quite slow in terms of SSD servers, where the servers can last easily close to a decade compared to HDD, which facilitates three to five years of working time. It is due to the absence of rotating parts that increase the endurance and enhance data integrity of SSD type, and data remains intact in SSD even when there is no power supply.

There is a downside to the SSD drives as they have a finite number of writes until it requires to be replaced. There are close to 3,000 write cycles on an average before breakdown. As HDD is built for writing data 24/7, they have comparatively more durability but also require more maintenance.

Power usage

While choosing a cloud plan, there has to be a proper research analysis done to rectify the needs and expectations that need to be satisfied from a cloud servers solution. Power usage is an essential deciding factor to weigh out the additional costs that go into maintaining the servers.

HDD drives tend to generate more heat due to their writing capacity, increasing overall power usage. SSD performs better because of its idle state and has a considerably low consumption rate.


HDDs are preferred mainly due to being cost-effective but fighting to compete against the server space SSD offers. The lower costs offered with large HDDs have competed against the new range of SSD with medium capacities. The cost is marginally dependent on the needs and budget capacity of a business.

SSD proves to be the most viable option in terms of durability and writing speeds, and overall performance over time. SSD also offers an excellent reduction in server load and processing time.


Businesses and individuals must have a good look at the product range and decide the solutions that are best suited for their needs. No matter the size requirement, everyone needs a cloud-based solution as it holds a lot of potentials and provides a gateway to better communication and integration.

For more information or Register for a free trial

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

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.


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
  • 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:

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure