E2E Networks' comments on TRAI’s Paper on Regulatory Framework for Over-the-top (OTT) services dated March 27, 2015

April 28, 2015

1: Is it too early to establish a regulatory framework for Internet/OTT services, since internet penetration is still evolving, access speeds are generally low and there is limited coverage of high-speed broadband in the country? Or, should some beginning be made now with a regulatory framework that could be adapted to changes in the future? Please comment with justifications.Answer: We object to the telco invented word OTT. There is no definition possible for what constitutes an OTT. If every application on Internet is an OTT that includes personal blogs, curated content websites run by SME's then we have a major issue with regulation. Such regulation would require humungous enforcement machinery.Question 2: Should the Internet/OTT players offering communication services (voice, messaging and video call services through applications (resident either in the country or outside) be brought under the licensing regime? Please comment with justifications.Answer: There are potentially millions of applications out there that would have some overlap with some functionality offered by a Telecom player in India. It would be an ill thought move to even think this is possible without damaging the Internet experience for everyone in India. How do you know the future ? Are you going to kill the next big thing from India with the potential to add a few %age points to the GDP by bringing back the license-quota raj in this country and that too in a sunrise industry ?Question 3: Is the growth of Internet/OTT impacting the traditional revenue stream of Telecom operators/Telecom operators? If so, is the increase in data revenues of the Telecom Operators sufficient to compensate for this impact? Please comment with reasons.Answer: Why is it the responsibility of TRAI to ensure revenues for Telco's at the expense of everybody else in the ecosystem. More application creators in India would result in more data usage via telecom networks and result in sufficient revenue for data services provisioning to remain profitable. Nobody has asked telco's to sell their services at a loss. There is not sufficient wired last mile by pure play ISPs in India for people to power Internet on their smart phones it is mostly going via the telcos. Let us assume for a moment that the voice revenue becomes zero, nothing prevents telcos from recovering the entire cost of their operations from selling data packs for Internet access at a price at which this cost is recoverable. In this oligopoly of operators the end customers have no choice but to pay for the price charged.Question 4: Should the Internet/OTT players pay for use of the Telecom Operators network over and above data charges paid by consumers? If yes, what pricing options can be adopted? Could such options include prices based on bandwidth consumption? Can prices be used as a means of product/service differentiation? Please comment with justifications.Answer: Telecom operators already sell Internet bandwidth to cloud computing players like us in India who provide the Internet-accessible compute infrastructure to Internet centric workloads. The end users already pay for Internet bandwidth by buying data packs from telcos. Where is the question of additionally charging a website/mobile application service operator additional arise ? Internet is a collection of Autonomous Systems ( network of networks ). Where everyone agrees to connect with each other either directly or via a routed path via one or more backbone providers in the world. While it is the right of AS'es to choose to connect with one another, in general everyone agrees that unless the entire Internet is made accessible to end users in a non-discriminatory fashion it would significantly reduce the net-benefit derived out of Internet-access for an end customer if a telco providing Internet access were to interfere with the content/applications being accessed by their subscribers. The market power of a largish telco over a single subscriber getting a much crappier version of Internet is a situation that a regulatory body must strive to avoid by nipping such ideas in the bud.Question 5: Do you agree that imbalances exist in the regulatory environment in the operation of Internet/OTT players? If so, what should be the framework to address these issues? How can the prevailing laws and regulations be applied to Internet/OTT players (who operate in the virtual world) and compliance enforced? What could be the impact on the economy? Please comment with justifications.Answer: No, we don't agree. If a web/mobile application is illegal in India then get the competent court to ban it. The ISPs including telcos are already capable of doing that.Question 6: How should the security concerns be addressed with regard to Internet/OTT players providing communication services? What security conditions such as maintaining data records, logs etc. need to be mandated for such Internet/OTT players? And, how can compliance with these conditions be ensured if the applications of such Internet/OTT players reside outside the country? Please comment with justifications.Answer: There is not much that can be done to prevent two terrorists from communicating securely via their own proprietary protocols running on top of Internet, Postal communications, voice telephony ( via use of modems transmitting what could be considered gibberish ) in a reasonable amount of time. What the law enforcement agencies should invest into is instead signal intelligence capabilities which can look at the publicly available data sources from the front door of social media/news media/online forums etc. and start making sense of it by investing into big data technology.The open source encryption and repudiation technology relying on open general purpose computing hardware today is sufficiently advanced that government projects like carnivore ( US ) are useless against it. It is pointless to put a massive regulatory burden onto very small startups for possibility of future abuse of services. There is only result of a huge regulatory burden on any industry as shown by the ISP industry in India. OSS'ification into a bunch of liasoning players instead of a vibrant and dynamic bunch who lead the nation into greatness.Question 7: How should the Internet/OTT players offering app services ensure security, safety and privacy of the consumer? How should they ensure protection of consumer interest? Please comment with justificationsAnswer: None. The regulator must first show a market failure before proceeding with an intervention. Do we see egregious failure of market in protecting privacy of end customers. Issue non-compulsory guidelines first for the Internet industry to get its act together if there is substantive failure to protect the privacy of end customers.Question 8: In what manner can the proposals for a regulatory framework for OTTs in India draw from those of ETNO, referred to in para 4.23 or the best practices summarised in para 4.29? And, what practices should be proscribed by regulatory fiat? Please comment with justifications.Answer: Who is ETNO and HOW is it a stakeholder in India ? Why should any stakeholder be required to comment on whatever ETNO is proposing ?Question 9: What are your views on net-neutrality in the Indian context? How should the various principles discussed in para 5.47 be dealt with? Please comment with justifications.Answer: Without net-neutrality participants with substantial market-power win automatically and the end customer loses. Net-Neutrality is a fairly simple concept. Don't interfere with the data coming through the pipe. There is a finite low cost today of reaching out to vast audiences on Internet in theory making it possible for a student sitting in a hostel to compete in a large niche market for SAAS/Mobile applications against large corporations with deep pockets. Preferential treatment to certain Internet applications based on a telco who is not direct service provider for an application by merely by an act of interfering with data passing through its network is a recipe for abuse of market power. While on the other hand it is perfectly fine for an Internet application provider to take direct services from any Autonomous System ( network ) on the Internet with a view to reduce number of routing hops to make its applications reach end customers faster or use a CDN service that does the same on its behalf as long as that doesn't result in others becoming slower ( not comparatively but actually ) or in-accessible ( e.g. Airtel-Zero, Internet.org) due its collusion with ISPs serving end customers.Question 10: What forms of discrimination or traffic management practices are reasonable and consistent with a pragmatic approach? What should or can be permitted? Please comment with justifications.Answer: What is generally considered Abusive Traffic which is not requested by actual customers e.g. Malware being served from a compromised server accessible on public Internet, DDoS traffic choking networks, email spam at large volumes which affects network's primary function of providing Internet Access to the end customer should be dropped from the network without further reference to any regulation. Most ISPs/Telcos already do that. Further prioritisation of traffic by use of deep packet inspection should be prohibited by law.Question 11: Should the Telecom Operators be mandated to publish various traffic management techniques used for different OTT applications? Is this a sufficient condition to ensure transparency and a fair regulatory regime?Answer: It is desirable that Telecom Operators public traffic management techniques they use with a view to describe their actual services to their end customers and interconnecting networks but it shouldn't be for the purpose mentioned here and not due to force of laws. Nor does such transparency even if enabled by force for laws results in a free and fair market.Question 12: How should the conducive and balanced environment be created such that Telecom Operators are able to invest in network infrastructure and CAPs are able to innovate and grow? Who should bear the network upgradation costs? Please comment with justifications.Answer: In a free market there would be players who figure out the unit economics better than others. Some might be profitable others would make losses. How are you as a regulator concerned with these questions. If the network usage becomes higher then so do the charges paid by end customers. The end users who are customers of telcos are eventually bearing the network upgradation costs.Question 13: Should Telecom Operators be allowed to implement non-price based discrimination of services? If so, under what circumstances are such practices acceptable? What restrictions, if any, need to be placed so that such measures are not abused? What measures should be adopted to ensure transparency to consumers? Please comment with justifications.Answer: Same answer as answer to question number 9.Question 14: Is there a justification for allowing differential pricing for data access and OTT communication services? If so, what changes need to be brought about in the present tariff and regulatory framework for telecommunication services in the country? Please comment with justifications.Answer: Same answer as answer to question number 9.Question 15: Should OTT communication service players be treated as Bulk User of Telecom Services (BuTS)? How should the framework be structured to prevent any discrimination and protect stakeholders interest? Please comment with justification.Answer: Same answer as answer to question number 9.Question 16: What framework should be adopted to encourage India specific OTT apps? Please comment with justifications.Answer: Reduce the regulatory burden on startups in India. Here I am founder of a small startup trying to pre-empt a regulatory event of the sort that pre-maturely caused Taxiforsure 's acquisition by their well-funded competitor. Why should I not be spending my time in generating growth for my cloud computing business instead. Liberalisation and reforms hold the answer to the question.Question 17: If the App based/OTT communication service players are to be licensed, should they be categorised as ASP or CSP? If so, what should be the framework? Please comment with justifications.Answer: Same as answer to the questions number 2. Communication between people is an essential part of majority of Internet applications today. Should every application ever built have to decide to kill off the network effects by removing all inter-end-user communication component from it to avoid licensing then you my dear friends at TRAI have dug the biggest graveyard for Indian startups.Question 18: Is there a need to regulate subscription charges for App based/OTT communication services? Please comment with justifications.Answer: Where do you see the market failure. There is no need for heavy hand of regulation unless there is a market failure due to which one particular player in the entire segment controls 90% of the market due to anti-competitive behaviour e.g. By violating network neutrality.Question 19: What steps should be taken by the Government for regulation of non-communication App based/OTT players? Please comment with justifications.Answer: Please justify why do you want to regulate every engineer who can write a few lines of code or use ready made open source code to produce something useful for other people by making it accessible over Internet. The government/regulators should really stay out of creating additional regulation unless there is a demonstrated market failure. Do you even realise how many %age points of GDP growth you are going to shave off from Indian economy by coming up with such ridiculous questions.Question 20: Are there any other issues that have a bearing on the subject discussed?Answer: India has one of the slowest Internet speeds in Asia region and the most expensive bandwidth even for bulk customers like us in Cloud Computing Space. Lack of network neutrality in India would lead players like us who can and are bringing content/applications back to Indian datacenter locations to shutdown shop and work for providing services for other global cloud computing providers instead of making cloud computing in India. We already work with serious disadvantages like high cost of power and cooling in India. The oligopolic mess in International Internet transit and domestic landscape dominated by liasoning players who most end users hate is the result of incompetent regulation and lack of forward thinking for last decade and a half since Internet was introduced into India.]]>Check the pricing of our offerings here

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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 –

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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. 

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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.






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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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