E2E Offering Nvidia A100 Tensor Core GPU Services

November 20, 2020


E2E networks offer a very high-performance cloud infrastructure and are a world-class cloud in India.

Their GPU Cloud is apt for a wide variety of applications comprising Computer Vision, AI, Scientific Research, Computational Finance, and Big Data. Taking the extensive benefit of GPU cloud computing, E2E networks announced its GPU power-driven cloud provision in association with NVIDIA.

In a world where cloud computing is the future and deep neural networks are being used extensively in the business, data scientists, scholars, and engineers can stop worrying about their precious time, money and memory, and concentrate on the next big AI innovation. E2E networks always intend on evolving and providing profitable cloud solutions for businesses. This article is about how E2E is offering powerful Nvidia A100 Tensor Core GPU Services and how important it is for all types of consumers extending from startups to big industries, from ML experts to CXOs of tech companies, and even tech enthusiasts.

Why do we need GPU Services?

When any data scientist wants to achieve high performance while training enormous datasets or deep learning (DL) models, most of the time, they take hours or even weeks without a powerful GPU.

While DL models require a lot of computational power to run, Machine Learning (ML) and recognition algorithms require great accuracy. Even in our digital world, high-res images and videos consume a lot of computational cost and storage. GPU driven clouds solve all such problems.


NGC can achieve optimal solutions for all existing problems while executing scientific computing and deep learning models and is very technologically advanced. NGC involves an extensive directory of GPU – powered packages essential for ML, DL, and High-Performance Computing (HPC).

Source: HPC in NVIDIA

NGC containers deliver an easy and powerful stage for deploying software systems efficiently to achieve faster and more efficient solutions. NGC allows operators to emphasize on developing slender models, congregating quicker intuitions, and creating optimum resolutions.

The key factors on which they rely are: -

  • Staying up to date
  • Faster innovation
  • Running anywhere

The benefits of using NGC are: -

  1. It provides models that are already trained and help deliver streamlined SDKs, thus enabling endwise AI explications.
  2. It has dedicated Augmented DL Frameworks, which make deep learning gear easily accessible. 
  3. It has renowned DL Stack Containers such as TensorFlow (Powerful library for data flow and mathematical computations), MXNET (framework for deep neural networks), and framework customizations.

Nvidia A100 Tensor Core GPUs in E2E

Source: E2E Networks

The NVIDIA A100 is capable of carrying out unparalleled acceleration. It can boost performance in big data, data analytics, artificial intelligence, and high-performance computing (HPC), and confront the most difficult computations easily. The A100 is capable of scaling up effectively to thousands of GPUs or being segregated into 7 GPU instances to boost jobs of every dimension using NVIDIA Multi-Instance GPU (MIG) technology.

The A100 is targeting to achieve a big milestone with 3,456 FP64 CUDA cores, 6,912 FP32 CUDA cores, and 422 3


– Gen Tensor cores. There is no alteration in code due to the combination of the exactness of FP16 and the range of FP32 during model training.

The NVIDIA A100 GPU is powered by the latest technologies like: -

  • Fine-grained Designed Thinness: 2X the computational output for deep neural networks.
  • 54 Billion Transistors (Xtors) on top of an 826 mm^2 size of a die: A 7 – nm processor, almost 3 times the speed of RTX 2080 Ti.  
  • 3rd Gen NVLink: Delivers connection-level fault finding and packet rerun mechanism.
  • 3rd - Gen Tensor Cores
  • Multi-instance GPU: It allows the partition of the A100 into seven distinct GPU occurrences.

Wrapping Up

The A100 GPU familiarizes revolutionary and innovative features designed to enhance implication loads. It offers extraordinary adaptability, stability, and performance. It can train huge Artificial Intelligence programs like BERT (Language Model) on a cluster of 210 A100s in only 37 minutes. It can also fast-track an entire range of accuracies, ranging from (floating point) FP32 to FP16 to INT8 and way down to INT4.

Looking at the HPC Performance Curve, we can see how it has delivered 9X more performance since 2016: -

Source: Nvidia

Speaking of Cloud Gaming, latency, or the delay in time between input and output, is a crucial part. E2E GPU cloud improves the gaming experience by delivering an ultra-low latency system to “Cloud Hunt” Users.  

Thus, E2E offering NVIDIA A100 Tensor Core GPU services are capable of giving a boost in: -

  1. Cloud data centres
  2. Supercomputers
  3. Single and Multi-GPU Workstations
  4. Servers and clusters, and
  5. Edge computing systems.                                

We are building a strong appreciation from the end-users because of our trustworthiness, scalability, affordability, and improved privacy features.

For free trial please click here :- http://bit.ly/3hhaiJm

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This is a decorative image for Project Management for AI-ML-DL Projects
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:





This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
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.






This is a decorative image for Top 7 AI Startups in Education Industry
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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