How Startups Have Benefited From Advancements in AI

February 5, 2021

Today, at least one out of 10 companies is using artificial intelligence technology. From a chatbot to security applications, AI is helping companies step ahead to solve real-world issues. Every major tech company is allotting enormous resources to explore possibilities on how artificial intelligence can improve its operations and solutions. Personal assistants like Google Assistant and Siri are now a part of our daily lives. Soon we might even see revolutionary self-driving cars on our roads. 

Multiple startups are using innovative solutions to improve their technologies and solve problems. Artificial intelligence helps perform complex tasks, cut down company costs, generate valuable customer data, and provide improved user experiences. AI can also help make faster decisions and avoid human error. It can help companies offer personalized experiences to the customers and even mine data to generate quality leads and increase sales. As the volumes of datasets increase, GPU and cloud computing technologies would be more widely adopted by startups. International Data Corporation has estimated that about $57.6 million would be invested in Artificial Intelligence and machine learning by 2021. 

Applications of Artificial Intelligence in Healthcare Startups

Various startups are developing AI-powered solutions that are backed by GPUs for faster implementation and efficient functioning. One such example is “vision for the blind,” in which AI-powered spectacles were developed for blind people. GPU backed the AI solution. Those spectacles were faster in implementing commands and operations than other similar solutions, and they responded within 5 seconds of scanning. Artificial Intelligence can also quickly scan through millions of images and identify potential abnormalities and patterns more accurately than a human doctor. This can highly reduce the risks of overlooking problems and misdiagnosis.

Artificial intelligence in the healthcare industry is expected to be worth 6 billion dollars in the coming future. Now, this combination is intimidating as well as threatening for the medical workers. However, there is no doubt that artificial intelligence can prove to be a game-changer for saving lives and treating health issues. From providing better care to predictive solutions, AI can be a part of all. It is impossible for a human being to completely eliminate errors and give 24-hour care and surveillance for patients. However, artificial intelligence can achieve it at a lesser cost. 

New startups are constantly working to develop these solutions and improve the healthcare industry. COVID-19 prompted many young startups to create innovative solutions to battle the pandemic, and the world saw them rise. Companies in the pharma industry also use AI. AI can help scan prescriptions and labels, prescribe drugs, and validate the authenticity and use cases of drugs. AI can make robotic applications compelling by enabling them to automate inspection and give real-time insights from data and more. 

Artificial intelligence can also help replicate human emotions and even create humanoids. Humanoids are a breakthrough in the field of robotics and artificial intelligence. If machines can understand human emotions and crack their body language, this technology can be utilized for applications such as lie detection and even interviews.

Artificial Intelligence Advancements Boosting Drones and Robotics

Drone technology startups are also being benefited from artificial intelligence by automating remote sensing and obtaining real-time data that is extremely useful for security and monitoring applications. The use of AI also minimizes human effort, and there is also a lesser possibility of error. The NVIDIA Jetson platform allows startups to develop and deploy artificial intelligence-powered robots, drones, and other autonomous machines. It allows a greater level of manipulation for robots, identifies defects, and ensures product quality, and it also enables local processing of sensors and data. One highly valuable use case is real-time video analytics that can provide real-time insights for the target area. The robotics and drone industry is experiencing a 19.6% annual growth rate.)

One example of a drone using AI is the Jetson TX2 which consists of 256 GPU cores and has the capability of performing 1.3 trillion operations per second. This drone utilizes nine custom deep neural networks to help it track up to 10 objects even while travelling at high speeds. And it can even help generate a 3D point cloud of a million points per second.

Various startups such as Grammarly, Ascent, Tempus, Narrative Science, Alphasense, Clarify, and more are using artificial intelligence, machine learning, deep learning, GPU cloud servers, and big data to power their platforms and deliver high-quality results.

Artificial Intelligence Technology Powered by GPUs

Artificial intelligence was created for the advancement of human life. Artificial intelligence, coupled with intelligent machinery, robotics, the internet of things, big data, and GPU, can help researchers and companies step beyond their limits and innovate new and better models. A Graphics Processing Unit (GPU) is a processor used for graphical and mathematical calculations, and it performs the activities required to deliver designs. GPUs used by E2E Networks are created for Artificial Intelligence applications and can handle massive calculations simultaneously. 

GPUs are economically feasible, and they enhance machine functioning. E2E cloud achieves high performance with the help of NVIDIAs powerful GPU. GPUs can be highly useful for machine learning applications as they can help machines analyze quicker and make complex decisions easily without human interference. Moreover, GPUs have greater memory and are faster than CPUs. The NVIDIA GPUs are considered the best GPU for machine learning as they have various libraries and integration with common frameworks such as PyTorch and TensorFlow.

Artificial Intelligence in Supercomputing

AI supercomputing is also helping tech companies and IT professionals enhance productivity and provide robust solutions for their customers. For AI supercomputing, we need to get the data, prepare the data, train the model, test data, and improve its accuracy. Modern supercomputers with NVIDIA GPU A100 are capable of training enormous artificial intelligence models. Supercomputers that use NVIDIA’s new GPU can be helpful for exciting applications such as computer vision and natural language processing. In a supercomputer during a task, GPU stimulates the computing of workload by using machine learning and artificial intelligence capabilities. It also supports heavy data processing.

E2E Networks is Enabling AI Startups to Create Revolutionary Innovations

Artificial-intelligence neural networks and cloud computing-based technologies are growing rapidly. The amalgamation of neural networks with cloud computing is a crucial component that facilitates research and development. Many companies are implementing GPU cloud servers for efficient and faster operations. E2E Networks is a public cloud provider and aims to develop cost-effective cloud-based solutions for companies. E2E Networks provides the best GPUs for deep learning and offer superior solutions in terms of cost as well as performance. GPU powers all the solutions that are provided by E2E Networks. Libraries that NVIDIA provides are also known as CUDA toolkits, and they enable easy processing during deep learning applications. Along with the GPU power, the libraries are created by a vast community at NVIDIA, and they offer many frameworks like PyTorch and Cafe 2.

E2E Networks has introduced its own GPU-powered cloud services. For example, the NVIDIA GPU cloud is a public cloud platform designed for scientific computing and deep learning. It consists of a wide range of GPU-accelerated software essential for deep learning, high-performance computing, and machine learning. NVIDIA GPU cloud provides an extremely powerful and simplified platform to develop the software for faster results. In the GPU industry, Nvidia is the leading vendor, with a 56% market share in 2019, and is followed by AMD with a 26% market share and Intel with an 18% GPU market share. 

NVIDIA Tesla GPU is the most advanced data centre GPU that is ever built to accelerate artificial intelligence high-performance computing and data science. It is comparable to the performance offered by 100 CPUs in a single GPU. NVIDIA Tesla GPU can help engineers and researchers work faster and bring the next innovations. E2E Networks allows contract-less cloud computing for Indian startups and SMEs. Thousands of customers trust it, and E2E GPU offers low latency and cuts the cost by up to 70%.

Today many startups are working on artificial intelligence. However, if the startup team is not very familiar with the AI world, their development would be slow. The NVIDIA GPU cloud provides a platform to build AI software and utilize GPU power. NVIDIA GPU cloud also provides pre-trained models to help data scientists build their models faster and provide customized SDKs to help develop and enable complete end to end AI solutions.

How Can Cloud-based AI Solutions Prove to be Beneficial?

Cloud computing has enabled new businesses to take advantage of AI solutions. This has led to rapidly developing technologies and increasing opportunities. Here is a list of how cloud-based solutions can be beneficial for companies:

1. Better Customer Experience

Artificial intelligence has helped companies provide personalized experiences to customers based on their past behaviour and engagement. Now, this helps streamline marketing efforts and reduce costs. Moreover, it improves customer engagement, and it also improves customer loyalty, ultimately increasing sales. Artificial intelligence does that by identifying the patterns in customer behaviour while browsing the site and making purchases. Millions and billions of transactions are stored in the GPU Cloud Service and analyzed. It also helps provide accurate recommendations and offers for individual customers.

2. Automation to Reduce Repetitive Working Hours

Artificial intelligence is also used in automating various processes to minimize working hours. With technologies such as automated emails and chatbots, companies can communicate with their customers without any human involvement. AI can analyze the data collected from the previous communications and respond accurately to further queries from the customers. When we combine machine learning with AI, the technology becomes better and better. And using AI chatbots, we can communicate with an unlimited number of customers simultaneously on a website or app.

Artificial intelligence can also be used to instruct robots in factories or maintain ideal conditions for optimal processing. In many countries like Japan and China, AI-based robots are being used to serve as receptionists and waiters. AI-based robots are also being used to deal with customer inquiries and even catch criminals.

3. Real-Time Assistance

Artificial intelligence also helps companies provide real-time assistance to their customers. AI systems can handle multiple queries at once and talk to millions of customers in a day. It can send personalized information and notification to the customers and also provide tracking information to customers.

4. Data Collection

Cloud-based solutions can also help companies get massive amounts of valuable data and process it. The analyzed data can be used to discover useful insights that can help the startups get an advantage over the existing competitors. The collected data can also help marketing teams create more impactful marketing strategies.

5. Predictions

Artificial intelligence, coupled with data analysis, can identify historical patterns to predict future outcomes. For example, AI can help store product potential sales and revenues for a season or a month. This can be really useful when planning the budget and stocking up the inventory. The predictions can significantly help cut costs and also give us insights into potential investment opportunities.

Artificial Intelligence has helped us derive insights from the unthinkable and has helped transform industries. From personalized customer journeys to predicting the next disasters, artificial intelligence is capable of doing it all. Artificial Intelligence can significantly impact businesses to save time and money by optimizing and automating tasks. And being machines, they can work 24 hours and at a much faster rate hence increasing productivity and efficiency. 

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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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