Here's What Nobody Tells You About Application Deployment On Cloud

March 25, 2019

As per IDC, the expenditure of cloud-specific services is expected to grow more than six times by 2020 compared to general IT spending as the adoption rate of cloud-computing systems is increasing. An excellent example of big companies utilizing the power of Cloud is Netflix. Their transition and transformation journey from legacy systems to cloud took about 7 years but the company is reaping benefits now in the form of increased service availability.Enterprises and their business applications are often backed with custom IT systems as they require heavy processing, customized configuration as well as resources. A big downfall of this system is that it requires extra manpower to be employed for managing these systems. But times are changing and so is the functioning of enterprises. Slowly adapting the cloud generation, startups and SMEs are leaning towards moving their existing applications as well as deploying new ones on a reliable cloud server.

Why moving apps directly to Cloud isn’t enough?

Hosting and deploying applications on cloud allows you to reap benefits like direct data sync, increased data accessibility, reduced costs, and redundancy etc. Though enterprises are readily accepting cloud for their upcoming applications, shifting existing apps to Cloud is not easy. Legacy IT systems that form the basis of large business applications are monolithic in nature. According to independent research conducted by Vanson Bourne, 47% companies agree that disparate data systems impact their organization in a negative manner while innovating, developing and reaching to markets with their services and products.They are also configured as per the static capacity of data centers which makes them even more unfavorable for utilizing the dynamic features of Cloud just by simply shifting the existing application. Moreover, in the case of deploying new applications on Cloud, enterprises need to train their overall teams accordingly as they were originally trained to work on traditional IT frameworks.Though application developers understand the dynamics of changing technologies like cloud computing, they might not be aware of the ideal approach that can reap optimum benefits. Here’s a detailed description of all the aspects that need to be kept in mind while choosing a cloud provider for deploying applications on Cloud.

Things to Consider For Deploying Applications on Cloud

The Ideal Platform for App Deployment

Cloud computing offers three models for hosting or deploying applications on a cloud server namely, IaaS (Infrastructure as a service), PaaS (Platform as a service) and SaaS (Software as a service). Depending on your business needs, you can choose the most suitable platform. Considering the usage of businesses, application developers and similar business, Cloud migration makes more sense for most of the established businesses.Cloud migration refers to the process of shifting your current services, data and other business components from on-premise to the cloud. It serves as an ideal choice because it allows you to avoid issues of vendor lock-in as you can always switch from one cloud provider to another based on your needs. As per recent studies more than one-third data will pass through the cloud and therefore, it is better to be prepared than to wait and watch. You should consider cloud migration services for your business as it provides you with the following benefits:

  • Lower costs of infrastructure with the use of cloud
  • Increased business agility
  • Aids in situations of disaster recovery
  • Helps you to be prepared for future needs
  • Allows you to upgrade your current IT systems

Moving Legacy Applications

While moving and deploying data and applications via cloud migration services can turn out to be extremely beneficial for your business; you need to keep a few factors in mind before going ahead with it for your business.

  • Your approach to cloud migration: In order to use cloud migration services effectively for your legacy apps, your first step is to determine how will you be using it for your organization. You need to conduct a software audit and check the status of all your existing applications.  

If the effort of migrating an app is higher than its output value, you can filter it out. You can retire all the redundant applications once you’re done with the audit. Based on this, you can also map different applications and find out the dependencies before the migration process.

  • Syncing development tools with Cloud: The next thing to check here is to consider if your development practices can fit in the cloud environment and if you shift the whole setup, what will be the access control policies for your code documentation. This step needs to be addressed if your development practices are leaned towards in-house development rather than using software tools. If you’ve developed your code using tools, syncing them on Cloud is fairly easy.
  • On-Premise resource integration- Business software often integrate with other applications and resources such as databases for information fetching. When moving performing cloud migration, you need to figure out how these application resources will be accessed. For example, if client applications use a virtual private network to access on-premise elements due to security reasons, you need to check with your cloud service provider to ensure your needs.  
  • Supported development stack- Technology is evolving with growing needs and so are cloud server and migration offerings. Based on your choice of technology stack including languages and databases for your existing application development needs, you need to carefully choose a cloud provider that supports your application stack completely. Shifting your application to Cloud should offer easy and smooth development for your applications.

Selecting a Cloud Service Provider

Whether you need to shift an existing application to cloud or are looking for a cloud solution for deploying new applications, the most important thing to consider is the ideal cloud provider. With the expansion of cloud service providers over the years, the competition has also increased.But this doesn’t mean that the most popular provider could be suitable for your needs. You need to fix your requirements first and then thoroughly evaluate a suitable vendor that could provide you the best cloud server accordingly.

  • The most valuable trait of cloud computing is that it allows you to deploy your application at minimum or low cost throughout the world. When choosing a cloud service provider, you must consider an option that allows you to deploy your application without any scalability issues. This way, your audience can access your application in a fast and easy manner.
  • Another important factor to be aware of is vendor lock-in. Many cloud service providers might seem attractive to you as they provide you with proprietary APIs with built-in code. Choosing a provider based on this option might lead you to end up with vendor lock-in and can become difficult to move your application anywhere else when needed. Therefore, instead of choosing these as an easy go option, you should always stick to the technology stacks that you’ve already worked on and are comfortable with.


Deploying applications on Cloud seems like an easy option and while it does take a lot of burden off your shoulders, there are a few prerequisites to keep in mind before executing the final step.

  • Figure out the path through which all members of the system including the team and the customers access information. Based on this information, create workflows that make the process easier and more accessible through Cloud.
  • Consider the job responsibilities of your team members and check for the chances of change in their roles due to this shift. You might want to signal the team about this priorly to avoid any end-minute chaos.
  • Deploy your application features through an iterative methodology rather than executing everything at once. This will prevent your users from getting overwhelmed
  • Cloud service providers update their applications according to the market and technology changes. Keeping this in mind, you must prepare your team for change management to avoid any hassle.
  • Test and deploy your application and product updates repeatedly through a designed process.


Deploying applications on Cloud can turn out to be successful for your legacy as well as new applications if you take care of the above-discussed aspects beforehand. These will help you in choosing a cloud service provider that meets all the necessary characteristics.

Latest Blogs
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:,product%20on%20the%20right%20platform.

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.

Reference Links:

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure