Top 5 Cloud GPU Offerings from E2E Cloud

October 28, 2020

Data Scientists, engineers and researchers are always looking for new inventions with the aid of deep learning technology. Deep learning technology requires large GPU powered computational power. In-house infrastructure management is quite expensive in case of construction and components purchasing. The best option is to use GPU powered Cloud services. GPU is well-known for speeding up the data, pipelining in neural networks, and other data components.

E2E’s GPU Cloud is appropriate for an extensive range of applications:

  • Artificial Intelligence: Here, you need GPU to train complex models at greater accuracy and speed to achieve predictions and decisions in algorithms. Greater the training samples, more accuracy is the detection.
  • Computer Vision: When it comes to computer-aided imaging, the quality of the model cannot be compromised. So a high GPU is required for deep-learning and better-quality analysis, medical imaging, and many other computer visions.
  • Computational Finance: As data handling by businesses is growing so is the requirement of precise financial calculation of complex financial data. Real-time calculation algorithms require large computation power, which GPU provides.
  • Scientific Research: In this year of pandemic, many scientists are making use of computational power for medicine detection. Here key components are time and greater bandwidth, to run deep search algorithms for real-time results, which is possible with the computational power of GPU powered cloud service.
  • Big Data: By making use of GPUs, large sets of data can be easily ingested into the system, enabling users to make powerful queries and real-time visuals, among billions of data sets.

Two immediate yields from choosing GPU than CPU powered are; DNN interfaces run 3-4 times faster and load management, and GPU powered network will aid to use higher load. To train and implement deep learning networks, you would require hundreds of data points. These data points need to be powered with significant resources, which include memory, storage, and processing power. Well-known benefits of GPUs on deep learning are:

  • A Higher Number of Cores: GPUs include a large number of cores, which can easily be clustered combined with CPUs, thus increasing the processing power.
  • Higher Memory: Larger bandwidth(up to 750GB/s vs 50GB/s) than CPUs, which aids the network to handle higher amounts of data in deep learning.
  • Task Distribution: Parallel processing power provides a platform for easy load distribution by constructing clusters. Or each GPU can be trained to power each algorithm.

Choosing the right GPU powered cloud, based on requirement is essential. All of the GPUs offered by E2E cloud are powered by Nvidia GPUs. The best reason to choose NVIDIA is because of the libraries that they provide, known as CUDA toolkits. This consists of libraries that enable easy processing in Deep learning based on strong machine learning. In addition to the GPU power, libraries are well-developed by the large community at NVIDIA and Frameworks like PyTorch, Café 2 etc. are offered.

E2E public cloud service provides a huge range of Cloud GPU Plans. Based on the combination of these 5 GPU offerings:

  • Tesla T4: T4 is based on TU104 NVIDIA graphics processing unit (GPU). Well-known for its AI projects supports all required AI frameworks, and network components consist of a universal deep learning accelerator perfect for distributed computing environments. While T4 provides innovative multi-precision performance to fast-track machine learning training and deep learning.
  • Tesla V100: V100 is one of the advanced data centre GPUs built to fast-track AI, Graphics and HPC. This is constructed and powered based on NVIDIA Volta architecture. A single V100 Tensor Core GPU believes in delivering the power of 32 CPU. It is armed with 640 Tensor Cores and known for higher efficiency with lower power consumption providing raw bandwidth of 900GB/s.
  • RTX8000: It is one of the most powerful graphics powered by NVIDIA Turing architecture and NVIDIA RTX. It is focused on power advanced graphical power during visualized based deep learning to extract complex models and scenes using physically accurate shadows, refractions and reflections to allow models with the instantaneous insight of imaging. It consists of 576 Tensor cores and 72 RT Cores providing 96 GB of GDDR6 memory and provides a bandwidth of 100 GB/s (bidirectional).
  • A100: It is one of the advanced data centres of GPUs. This Tensor Core GPU brings extraordinary acceleration required for AI, analytics, and HPC. It is powered by NVIDIA ampere architecture and delivers 312 teraflops (TFLOPS) of deep learning performance and raw bandwidth of 1.6TB/sec. Every deep learning network consists of Caffe2, MXnet, TensorFlow, Theano, Pytorch frameworks powering 700+ GPU accelerated applications.
  • NVIDIA virtual GPU (vGPU): As companies are using hypervisor-based server virtualization about 80% server workload is run on virtual machines (VM). vGPU is licenced software which enables to power your AI, ML and HPC workloads. vGPU provides the platform to power multiple VMs. All the other top GPUs are supported by vGPU supporting max of 16.


To advance rapidly, machine learning capabilities require greater processing capabilities. As divergent to CPUs, GPUs deliver higher processing power, larger memory bandwidth, and platform for parallelism. On-premise possessions typically come with a high upfront overhead, with the use of E2E cloud GPU you can,

  • Finish First: Stay ahead of the competitors by making use of the advanced computational power, ready state libraries and frameworks leveraging business throughputs.
  • Solve: Let your business problem be solved with precise measurement. Tensor core provides that best platform.
  • Save: Save that time and project budget by choosing the E2E GPU cloud service. By making use of ROI across each node.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:,product%20on%20the%20right%20platform.

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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