Data containers in NVIDIA GPU Cloud

February 28, 2022

What Is a Data Container?

A fact’s field is the answer for transportation of the database, required to run from one laptop device to another. A fact’s field is a facts shape that "shops and organizes digital objects (a digital item is a self-contained entity that includes each fact and approach to governing the facts)."

It is much like packaging a meal kit where the dealership purchases a container containing recipes, cooking tips, and the required components to make it handy to put together for consumption. Likewise, facts boxes keep and control the facts and deliver the configurations to extraordinary laptop structures for easy database setup and use.

“Containers offer fast, efficient, and smooth solutions which are deployed in a manner to address the infrastructure requirements. They additionally provide an opportunity to make use of digital machines.”

Docker, an unusual open-source tool, creates or defines the field timely by provisioning databases in an extraordinary fashion.

Other Definitions of a Data Container:

“A hassle-free option to get a software program to run reliably while moving from one computing environment to another.” -(CIO)

"A way to “offer procedure and personal isolation.” -(Paul Stanton)

“A socket which can make any facts inside a fact’s template accessible.” -(Delphix)

“A way to standardize bundle applications – consisting of the code, runtime, and libraries – and to run them throughout the software program to improve lifestyles cycle.” -(Gartner)

"An infrastructure that provides “speedy deployment in a light-weight framework … best for services associated with scaling up and down, speedy provisioning for improvement, and a critical part of many DevOps workflows.” (IBM)

Uses of Data Container:

  • To quickly deliver packages from the cloud to clients, and vice versa, whilst ensuring identical performance.
  • Ensuring development, testing, and manufacturing environments are similar; hence, lowering surprising behaviour.

Uses of Data Containers in Businesses:

  • To save setup time in shifting between pc surroundings.
  • To quickly transport large documents throughout a community.
  • To provide sources in a “simply in time” style that has the same utility functionality (e.g., supplying an internet browser with what it wishes to run a database-associated utility effectively)
  • Create and enforce microservices extra effectively.

Data Science & Machine Learning in Containers:

While constructing information technology and system studying powered merchandise, the research-improvement-manufacturing workflow is non-linear. It is similar to improving the conventional software programs, where the specifications and issues are (mostly) understood beforehand.

There is plenty of trial and error involved, together with the take a look at and use of recent algorithms, attempting new information versions (and coping with it), packaging the product for manufacturing, end-customers perspectives and perspectives, remarks loops, and more. These make one's task challenging.

Isolating the improvement surroundings from the manufacturing structures is needed to guarantee that your utility will paint. And so is placing your ML version improvement paintings into a field (Docker) that can assist with:

  • copying with the product improvement and
  • retaining your environment clean (and making it smooth to reset it).

Most importantly, shifting from improvement to manufacturing will become easier.

In this article, we will discuss the improvement of Machine Learning (ML) powered merchandise, in conjunction with high-quality practices, for the usage of packing containers. 

We will address the following topics:

  • Machine learning iterative approaches and dependency.
  • Version management in any respective stages.
  • ML Ops vs DevOps.
  • Need for equal dev and prod surroundings.
  • Essentials of Containers (meaning, scope, Docker report and Docker-compose etc.)
  • Jupyter pocketbook in packing containers.
  • Application improvement, with TensorFlow, in packing containers as microservice.
  • GPU & Docker.

What you need to know

To recognize the implementation of the system gaining knowledge of initiatives in containers, you should:

  • Have simple information about software program improvement with Docker.
  • Be capable of software in Python.
  • Be capable of constructing a simple system gaining knowledge of and deep knowledge of Fashions with TensorFlow or Keras.
  • Have deployed a minimum of one system to gain knowledge of models.

The following topics will be beneficial for you to understand Docker, Python, or TensorFlow:

  • Software improvement with Docker.
  • Python for beginners.
  • Deep knowledge of TensorFlow.

Machine learning iterative processes and dependency

Machine learning is an iterative process. When a toddler learns to walk, it repeats the procedures of walking, falling, standing, walking, and so on – till it “clicks”, making it walk.

A similar idea applies to studying the device, and it is essential to make sure that the ML version is shooting the required styles, traits, and interdependencies from the given data.

When you're constructing an ML-powered product or application, the iterative procedure needs to be organized, especially with device studying.

This iterative procedure is not always restricted to product layout alone, yet it covers the complete cycle of product improvement and the use of device studying.

The proper styles required by the set of rules to make commercial enterprise selections properly are hidden within the data. Data scientists and MLOps groups want to install many attempts to construct strong ML structures that can perform this task.

Iterative tactics may be confusing. As a rule of thumb, a regular device studying workflow must encompass at least these subsequent stages:

  • Data series or statistics engineering
  • EDA (Exploratory Data Analysis)
  • Pre-processing the data
  • Feature engineering
  • Model training
  • Model evaluation
  • Model tuning and debugging
  • Deployment

There might exist a right away for each stage, or an oblique dependency might exist on different stages.

Here is how I want to view the complete workflow primarily based totally on ranges of machine design:

  • The Model Level (becoming parameters): assuming that the statistics have been collected, EDA and simple preprocessing are complete, the iterative method starts offering solutions. If you have to pick the version that suits the problem you are attempting to solve. There isn't any shortcut, as the first-class fit can only be found via iterating through a few Fashions.
  • The Micro Level (tuning hyperparameters): you start a new iterative method on the micro-level when you pick a version (or set of Fashions) to get to the first-class hyperparameters.
  • The Macro Level (fixing your hassle): the primary version you construct for a problem will hardly ever be first-class viable, even if your program is flawless with cross-validation. It is because version parameters and tuning hyperparameters are the handiest components of a hassle-fixing workflow of a complete device. At this stage, there may be a want to iterate through a few strategies for enhancing the version of the hassle you are fixing. These strategies encompass attempting different fashions or resembling.
  • The Meta Level (enhancing your statistics): While improving your version (or educating the baseline), you could see that the statistics which you use are of negative quality (for example, mislabeled) or which you want extra commentary of a sure type (for example, pictures taken at night). In these conditions, enhancing your datasets and/or getting extra statistics turns out to be very critical. You must preserve the viability of the dataset to the hassle you're fixing.

These iterations will usually result in numerous adjustments to your machine, so model management is critical for green workflow and reproducibility.

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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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