Three Things You Need for Peace of Mind: Backups, Monitoring, and Redundancy

January 21, 2022

The three main points of peace of mind are backup, monitoring, and redundancy.
Firstly, let’s talk about backup.


A backup is a copy of vital data saved in a different location so that it may be restored if it is accidentally destroyed or corrupted. The frequency of backups is determined by how frequently data changes, how important the data is, and how long it takes to back up.
The most effective data backup methods are:

Back up on a regular basis
As they say, consistency is the key to success. Backup frequency and priority are largely reliant on your specific IT architecture and requirements. Assume your organization adds and produces many new files to a server every week. You're likely to lose a week's worth of production and morale if you lose a week's worth of progress. As a result, selecting a proper backup frequency is critical to enhancing the efficacy of your data backups. Organizations should strive for weekly backups at the very least to comply with data backup best practices. For optimum results, you should back up your data once a day.

Be sure to protect your backups
Your data backup is a clone of your "live" data, which you interact with on a daily basis. The backup files will be encrypted if the original data is encrypted. The issue emerges when these files are not encrypted. Viruses, spyware, and hackers can still obtain access to these backups and utilize your information to their advantage. To put it another way, backing up your data isn't the same as securing it. Consider ransomware, a common (though extremely hazardous) malware. It uses an unbreakable algorithm to lock you out of any file it can access through your computer.
Evaluate your backups

If your backups don't work when you need them the most, they're useless. Checking for file usability, application functioning, and database integrity is what testing your backups entails. Because testing your backups may be laborious and time-consuming, you'll want to plan ahead of time. It's preferable to test after many files have been uploaded or if major changes have occurred since the last backup.

Let’s jump on the other topic that is “Monitoring,” and why it is important for peace of mind.

A monitoring system is a piece of software that assists system administrators in keeping track of their infrastructure. These tools keep an eye on system devices, traffic, and applications and raise an alert if anything goes wrong.
The advantages of monitoring will help us to give the following benefits:
Efficiency in terms of cost
While disasters cannot be predicted, network problems may be anticipated. Downtime at any firm results in loss of revenue. Therefore avoiding it is the best strategy. Monitored services accomplish just that by keeping an eye on various network activities and catching issues before they happen. As a result, there will be no downtime and no financial loss, making it a cost-effective option.

Better safety
It's not only network issues that cause downtime; sometimes, it's a malicious attack. Whether an attempt is made to breach a corporate network or a rogue piece of malware tries to infiltrate, monitoring services will detect such unwelcome intruders and keep your organization safe.
Increased productivity
Monitoring services will identify such unwelcome visitors and keep your organization safe, whether an effort is made to enter a business network or a rogue piece of malware tries to infiltrate. They can help by making recommendations for infrastructural improvements or creating roadmaps for technology upgrades. As a result of having access to the most up-to-date technology, your team will be more productive across the board.

Reduced IT worries
Companies that monitor network traffic aren't only there to keep an eye on it. In other cases, an MSP might offer expertise to offload IT services while also handling the broader business aspect. IT difficulties, big or small, may eat up time and money, making it difficult for a workforce to focus on its goals. By eliminating these issues, your organization will be able to focus on its objectives while also ensuring that your IT is safe (assuming the provider is up to the task).
Let us now discuss redundancy.

Redundancy is based on the idea that objects in the universe have a propensity to break down and fail us exactly when we need them the most. It doesn't matter if you call it the 2nd Law of Thermodynamics, entropy, or Murphy's Law; the concept is the same. Redundancy is a response to Murphy's irritating Law, and it's designed to offer us peace of mind that we'll be able to weather any storm.

Single points of failure should be avoided
The fact that any component can fail at any time is at the heart of the problem. Preventive maintenance and proactive monitoring can assist in avoiding disaster, but relying on a single unit for a crucial function is a dangerous proposition. A single point of failure (SPOF) is a component that, if it fails, will bring the entire system down with it. It's also the worst thing that might happen to an IT system.

Various levels of redundancy are available
Redundancy is a notion that may be used for a variety of technologies at various levels. It all relies on the system's scope. In a small workplace, a basic database should be backed up or replicated, ideally offshore. Email servers should be duplicated across many servers and backed up on a regular basis. If a small firm just has one internet connection, they will be harmed if they lose access to crucial data.

Disaster Recovery Failover
Time is money in the high-stakes corporate environment. Every minute that internet service is unavailable costs business money in terms of service level agreement (SLA) fines and lost revenue. That is why having a reliable service failover mechanism is beneficial. When a primary system fails, and a backup system takes over, this is known as failover. The best failover solutions do not need human intervention.

Hence, peace of mind is hidden in these three things: Backup, Monitoring, and Redundancy.
“When technology brings people together, it is at its best.”

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