Stay free and open with Multicloud

October 13, 2021

Technology and information go hand-in-hand with speed, flexibility, and efficiency. From offline servers to cloud-oriented applications and the latest multicloud options, we’ve undoubtedly traveled significantly long. 

In the quest to minimize the response time and speed up business applications, a lot of effort was put into optimized functions and experiments in on-premise server infrastructure. Ultimately, the quest turned into the origination of cloud-based apps and recently crafted the path for a multi-cloud strategy. 

Why did the businesses feel the need for multicloud architecture? Well, we can converge the major factors into reduced response times and higher flexibility. This was led by enterprises that started updating from a cloud-oriented technology to a highly flexible and widespread framework.

Introduction to Multicloud

For organizations desiring to optimize their cloud architecture competence and abilities, a multicloud setup might be the most favorable approach. 

Multicloud simply relates to accessing multiple instances of various clouds provided by different vendors. In multicloud, businesses can utilize the features of different vendors. Multicloud packages the features, infrastructure, and other offerings together so that enterprises can have access to overall vendors, and data can be arranged in an architecture suitable to its abilities.

Why Multicloud?

Let’s try to figure out some reasons to adopt multi-cloud. Well, there exists a number of supportive parameters, including:

  • Prevent vendor lock-in for certain services
  • Well-confined disaster recovery
  • Effortless data transfer
  • Hassle-free scalability and flexibility
  • Grab the benefit of competitive pricing

Benefits of Multicloud: What Makes Multicloud a Worthy Choice?

An efficient service provider for your multicloud requirements is expected to be reliable enough to take over enterprise workloads through flexible and versatile services. The same includes executing heavy workloads, converged databases, or virtualization tasks. Additionally, enterprises accessing multicloud services can optimize service, price, and resources. At the same time, the quality requirements - high flexibility, data security, and interoperability are also entirely satisfied. It certainly takes a lot of effort to set up, but when done appropriately, multicloud deployment can truly empower an organization.

  • Business agility – Enterprises can speed up their priority processes and enhance user experiences. Overall, they can lead a systematic approach thanks to multicloud to have an advantage over others.
  • Business flexibility– Multicloud provides infrastructure flexibility to enterprises by blending on-premise server architecture with private and public cloud infrastructure. Overall, businesses get many operational and economic benefits.
  • On-demand needs- Organizations can rapidly satisfy the ever-rising needs of their business as there is no risk of depending on a single-cloud provider. 
  • Backup & recovery– The multi-cloud infrastructure offers a sophisticated way to keep critical data safe. Enterprises get backup and recovery capabilities that are essential against power outages, hardware damages, and other disasters. 
  • Security- Independent cloud providers take complete responsibility to secure their cloud infrastructure. High-level safety and security is maintained by assessment of the cloud from the providers. 
  • Network Performance - This enables enterprises to create and maintain low-latency, high-speed infrastructure that serves far better response times and enhanced user experience.  
  • Choice of workload distribution - Businesses have the option of distributing the workload among various cloud providers. The best part is that they no longer have to rely on single-cloud providers and hence lock-in with any terms & conditions. Additionally, utilizing multiple services helps in getting multi-year discounts from different providers.  
  • Best of infrastructure benefit – One of the basic benefits is that enterprises can use their expertise in relevant domains. Various cloud providers have expertise in niche areas so that businesses can acquire diverse capabilities. 
  • Competitive pricing – Businesses can compare different services and efficiencies to others. Also, they easily find the most economical options thanks to the availability of a vast number of choices. 
  • Excellent ROI – Enterprises can use various services offered by all the different clouds involved. What does it do? It assists businesses in finalizing which cloud is the ideal fit for ever-fluctuating infrastructure requirements. Hence, any one cloud from the multicloud architecture can help assist the shortcomings of another. Enterprises can utilize these various services for the good. They can potentially gain significant outcomes on multi-cloud investments. 
  • Low latency – It removes the small delay that users often face with applications (due to the high distance between the apps stored and access points). Multicloud fills this void by bringing the data center closest to end-users. What it does is that it lowers the number of server hops and offers a minor latency period for users’ requested data. 
  • Innovation –  Multi-cloud approach drives innovation for organizations. Undoubtedly, using a single cloud halts the utilization of technology and limits innovation. On the other hand, multicloud offers innovative solutions from different providers and their experts. 

Conclusion

Multicloud is starting a revolution and is here to stay. A large number of global enterprises have acquired this approach and operate approximately almost five clouds. Various trends are converging to push the drive.
Priorly, businesses are trying to become more organized in their utilization of the E2E cloud and moving from infrastructure hosting and automation to developing their platforms with cloud-native services in analytics, serverless development, AI, and industry-specific offerings. Additionally, organizations are relentlessly writing code and creating automation frameworks that need to run natively on multiple clouds. E2E Network offers world-class cloud services that accelerate businesses through powerful and reliable infrastructure. Keep checking this space for more informative and interesting content.

Signup here to know more – https://bit.ly/344Ai4a

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