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.

Latest Blogs
This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

Build on the most powerful infrastructure cloud

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