Frequently asked questions about implementing a Public cloud strategy

July 22, 2021

Your business is considering moving data or applications to the public cloud. Perhaps you are hoping to reduce capital expenditures (CapEx), spin up resources for new projects more quickly or simply reduce your on-premises IT infrastructure. Whatever your objectives, implementing or extending your public cloud strategy can raise a lot of questions. Below are some of the most frequently asked questions—and answers—about embracing the public cloud.

1) What benefits can I realistically expect from integrating public cloud into my IT strategy?

The top benefits of public cloud are:

• Virtually unlimited capacity. But you only pay for what you use. Expanding your resources in an on-premises data center incurs CapEx and can entail high running costs even as machines are under-utilized. The public cloud can flex to meet evolving needs or spikes in demand without the cost of machines sitting idle in your data center.

• Greater agility. You can increase, decrease and change the resources you need. This allows you to innovate faster, deliver new revenue-generating opportunities and improve workflow processes.

• Simplicity. Often you can manage application deployment through self-service portals, reducing the administrative
burden on your IT team.

• Access to the latest technology. Unlike your private cloud or on-premises data center, Cloud Service Providers (CSPs) can refresh and upgrade their infrastructure frequently, so you benefit from the latest hardware and software without having to buy it.

2) What should I consider when choosing a cloud service provider?

Make sure the CSP you choose understands your business objectives—and is committed to helping you meet them.

Share the goals and expectations of your public cloud strategy with your provider and work with them to define the solution that’s best for you.

It’s important to choose a cloud provider that can grow with your business as your needs expand and as you deploy new applications. Look for providers that build their services on highly scalable technologies.

It’s also important to ensure your applications perform responsively for your customers, workers, partners and suppliers.

That’s why we offer servers powered by industry-leading Intel® Xeon® processors that maximize performance for your cloud applications.

3) How do I know that my data and applications in the public cloud are secure?

Security is rightly a key consideration for organizations thinking of moving to the public cloud. Research shows, however, that the public cloud is typically more secure than an enterprise data center, and through 2020, public cloud data centers are expected to suffer 60 percent fewer security incidents than traditional data centers. Most CSPs have security enabled from the hardware layer, establishing a root of trust from the core outwards and making attacks as difficult as possible.

4) How do I choose which workloads and data should move to the public cloud?

There are no hard-and-fast rules about which workloads and data should reside in the public cloud versus your existing on-premises infrastructure. To identify the best candidates for successful cloud migration, follow a process similar to this:

  1. Map your organization’s applications and their dependencies to gain a clear picture of what is currently running in your data center.
  2. Create an inventory of your applications that includes: application type and version; operating system; server, storage and networking characteristics; security profile and rules; interdependencies; and performance requirements.
  3. Identify the applications that can be moved to the cloud with little re-engineering (“lift and shift”) and those that might require substantial re-architecting to migrate.
  4. Identify the applications that will deliver the greatest business benefits if moved to the cloud (for example, those with unpredictable or highly variable storage and networking requirements).
  5. Take advantage of existing models for optimal application placement, such as the Intel Affinity Model. The chart below lists common applications along the x-axis and applies an Attribute Score based on the typical data volume, integration, security, and performance requirements for each application. The Attribute Score is used to determine whether the workload tends to favor a public or private cloud deployment.

5) Which path to the cloud is right for my organization?

Implementing a public cloud strategy does not necessarily require that you move all your applications and data
wholesale to a CSP. Your strategy may focus on the public cloud, or you could take a hybrid cloud or multi-cloud approach.

Hybrid cloud: A computing environment that combines public cloud(s) and private cloud by allowing data and
applications to be shared between them. Organizations can benefit from seamlessly scaling on-premises
infrastructure to off-premises infrastructure. Hybrid cloud is a subset of multi-cloud.


Multi-cloud: A mix of public, private or hybrid cloud solutions (not necessarily using different cloud types). It may be a mix of private clouds or a number of public clouds provided by more than one CSP, or it may use a combination of
private and public cloud services. Multi-cloud allows organizations to combine best-of-breed solutions and services,
providing intelligent and dynamic allocation of resources and workloads to meet business requirements.

Here are key steps to consider when deciding on your path to the cloud:

  1. Assess the business objectives you want to achieve (e.g., reduce CapEx, deploy new projects quickly, improve
    scalability to support applications with variable demands).
  2. Determine the applications that are good candidates for migration to the public cloud.
  3. Identify the infrastructure you need to support the applications you prefer to keep on-premises.
  4. Work with your wider IT team and your chosen CSP to determine how to use the public cloud alongside your
    existing infrastructure. Based on your objectives and your application placement strategy, a multi-cloud or hybrid
    cloud approach may make sense.
  5. Plan your migration path carefully. Start by migrating low risk “lift and shift” applications to the public cloud, then
    use what you’ve learned to optimize the deployment and ongoing operational processes.

6) What are the cost parameters for migrating to the public cloud?

As with many IT decisions, the cost of your chosen cloud strategy will depend on your requirements and how you
choose to proceed. The main factors influencing the cost are:
• Scale and speed. How much capacity will you need from your CSP? What level of performance will your migrated
applications demand? Many vendors and manufacturers provide tools and methods to help you “size” your resource
needs. We can help with that effort.
• Value-added services. What additional services do you want your CSP to provide? Many cloud providers offer
managed services and additional security services for an extra fee.
• Private cloud. If you choose a multi-cloud or hybrid strategy, the factors to consider are the cost of the private cloud portion and the effort required of your IT staff. While many large companies choose to custom engineer their private cloud infrastructure, medium-sized and smaller businesses often choose assemble-to-order options or
converged/hyper-converged infrastructure solutions that reduce IT effort and costs.

One way to control costs is to start small with a single application or proof-of-concept pilot. Purchase just enough
infrastructure to meet that need and to get IT more comfortable with cloud migration. Then you can add more
workloads and infrastructure at your own pace.

7) How long will it take to implement my cloud strategy?

It’s important to remember that migrating to the public cloud or establishing a multi-cloud strategy doesn’t mean simply hitting the ON switch and instantly moving from your current data center to a public or multi-cloud infrastructure. Many companies make this journey in small increments (often one application at a time) and take care to learn from each step along the way.

Another thing to remember is that your cloud strategy will continue to evolve over time as your business needs shift and cloud technologies develop. It’s best to look at your cloud strategy as a long-term, dynamic part of your ongoing IT strategy.

8) How much expertise will my cloud strategy require from my IT staff?

Your IT staff will need to become aware of cloud operations, but the level of knowledge required can vary greatly
depending on the type of on-premises cloud infrastructure you deploy and how you plan to use the public cloud.
For your on-premises private cloud, a custom-engineered solution can require a great deal of time. But a hyperconverged infrastructure—where each resource has compute, storage, networking, virtualization, orchestration and manageability built-in—can be deployed quickly and requires only high-level knowledge about scaling and application deployment.

Hyperconverged systems are also designed to make infrastructure scaling easy, with minimal effort required to add new resources to the infrastructure pool.

We can help you make the right choices for your public cloud infrastructure, ensuring you get the performance and features you need based on your application requirements.

9) What makes Intel® technology right for the public cloud?

Intel innovations are already powering the majority of cloud servers, and for good reasons:

• The latest Intel® Xeon® Scalable processors deliver outstanding performance across a wide variety of modern
applications.
• These processors include advanced, built-in security features to help protect applications, data and infrastructure.

With any cloud strategy that takes advantage of Intel® technologies, you can be sure that your applications and data benefit from the performance, security, and resiliency you need to grow your business through digital innovation.

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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 –

  • 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:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

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:

Wiom

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. 

TechVantage

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

Manthan

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. 

NetraDyne

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.

 

Helpshift

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. 

Facilio

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.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

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:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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