Everything you need to know about distributed cloud computing

February 2, 2022

A few years ago, the cloud's core premise was mocked by reducing it to the concept of "someone else's computer," a phrase that can be found on the coffee cups of many IT workers. 

However, the following is a fundamental definition of cloud computing.

Cloud computing is a distributed digital infrastructure resource that provides internet-based hosted services. While there are many various ways to define cloud computing, the following four qualities are always present:

·         Networking

·         Data Management

·         Storage

·         Devices

What is the mechanism behind it?

Businesses can rent everything from applications to storage from a cloud service provider rather than having their own computing equipment or data centers.

Businesses may avoid the upfront expenses and complexity of establishing and maintaining their own IT infrastructure by paying only for what they need when using it, thanks to cloud computing.

As a result, cloud computing service providers can achieve massive economies of scale by offering the same services to a broad range of customers.

What is cloud computing's background?

The name "cloud computing" has been used since the early 2000s, but the concept of "computing-as-a-service" dates back to the 1960s, when computer bureaus gave businesses the option of renting time on a mainframe rather than buying one.

The emergence of the PC, which made owning a computer much cheaper, and subsequently the rise of corporate data centers, which allowed organizations to store massive amounts of data, completely eclipsed these 'time-sharing' services.

The concept of renting access to computer power returned in the late 1990s and early 2000s in the shape of application service providers, utility computing, and grid computing. Following that, cloud computing gained traction by introducing software as a service and hyper-scale cloud computing companies.

What is the significance of the cloud?

According to IDC information, constructing the framework to help distributed computing presently represents more than 33% of all IT spending worldwide. Meanwhile, traditional in-house IT investment continues to decline as computing workloads migrate to the cloud, whether through vendor-provided public cloud services or private cloud established businesses themselves.

According to 451 Research, organizations will spend over 33% of their IT financial plans on facilitating and cloud benefits this year, "demonstrating an expanded dependence on outside wellsprings of foundation, application, the executives, and security administrations."

Features of Cloud computing

·         Self-service provisioning: End users can instantly deploy compute resources for nearly any job. End users can provision computing resources such as server time and network storage, obviating the requirement for IT administrators to provision and manage to compute resources in the past.

·         Elasticity: Businesses can quickly scale up as their computer needs grow and down as they decline. This reduces the need for large-scale expenditures in local infrastructure that may or may not be operational in the future.

·         Pay-per-use: Users can pay only for the resources and workloads they utilize because compute resources are assessed at a granular level.

·         Workload resiliency: Cloud service providers frequently use redundant resources to ensure resilient storage and keep users' critical workloads running — typically across different global zones.

·         Organizations can transfer specific workloads to or from the cloud — or to multiple cloud platforms — as needed or automatically — to save money or take advantage of new services as they become available.

·         Broad network access: A user can access cloud data or upload data to the cloud from anywhere with an internet connection and any device.

·        Multi-tenancy and resource pooling: Multi-tenancy allows multiple customers to share the same physical infrastructure or apps while maintaining their privacy and security. Cloud providers use resource sharing to provide several clients using the same physical resources.

Advantages of Cloud Computing

·        Because interruption is uncommon with cloud computing, businesses don't have to waste time and money fixing problems that arise.

·         By storing data in the cloud, users can access it from any device with an internet connection from anywhere. Smartphones and other mobile devices can access company data, allowing remote employees to connect with coworkers and customers.

·         Data loss is a concern for all businesses. In the case of a calamity, such as a natural disaster or a power outage, cloud-based services allow businesses to restore their data quickly.

Limitations of Cloud computing 

·         Pay-as-you-go cloud subscription options, along with variable workloads, can make it challenging to define and anticipate final expenses.

·         Organizations are trying to keep up with the increased demand for tools and personnel with the appropriate skill sets and knowledge as cloud-supporting technologies advance at a rapid pace.

·         Multi-cloud installations can cause efforts to address more general cloud computing concerns to become disjointed.

·         Creating a private cloud is a challenging endeavor for IT organizations.


Cloud computing is still in its early phases of development, despite its long history. Many organizations are as yet choosing which applications to move and when they ought to do as such. However, as businesses become more comfortable with the idea of their data being stored somewhere other than a server in the basement, usage is only expected to rise. We're still in the early stages of cloud adoption; according to some estimates, only 10% of the workloads that could be moved have been done so far. 

The cost implications of shifting the rest of the undertaking registering portfolio to the cloud may be less clear. As a result, cloud computing companies are increasingly promoting cloud computing as a catalyst for digital transformation rather than focusing just on cost.

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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. 

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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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