The Best Roadmap to Building a Multi-Cloud Strategy

June 7, 2021

To ace the game in the business market, the foremost requirement is to be updated with all the data management technologies. One of those technologies is the Multi-Cloud system.

The market is saturated with numerous cloud computing vendors. There are businesses that are adamant to stick to the single cloud environment, but today the smart move would be to update your whole system in Multi-Cloud.

Multicloud is defined as the use of two or more clouds provided by different cloud vendors. It can be an emulsion of software as a service, infrastructure as a service, or platform as a service (SaaS, IaaS, Paas) different clouds, and distinctive qualities that are suitable for a certain type of workload. When you choose a multi-cloud environment, you basically are assigning suitable clouds for each set of requirements.

The question arises, why opt for Multicloud? The working of a company is built by putting together various departments. Each department has a group of people who are best suited for that kind of work. This way, you are employing a task-specific workforce that reaps top results. The Multi-cloud system works similarly, i.e., you can benefit from the unique capabilities of different cloud providers without actually relying on or shifting to a single cloud, which is a tedious task. A Multi-cloud environment also reduces the disaster damage situation to a great extent. If task-specific workloads are deployed to one cloud provider, in case of an outage, all your data goes down with it. In contrast, this is not the case with a Multi-cloud environment. To build an effective and successful Multi-cloud strategy, there are various aspects that need to be focused on, which are:

1. Reason the purpose of each cloud deployment In your business, while employing a Multi-cloud environment, be specific with the rationale of doing so. Each cloud deployed should have a specific purpose according to the requirements of your business infrastructure. For example, you can use one cloud server for emails regarding one supplier, one cloud server for retail and transactions purposes and another for storing data. What-so-ever be the employment reason, you must be well-versed with how each cloud deployment is helping our business to achieve mission-critical tasks. Also, there is no such sine qua non of assigning independent cloud servers to different purposes. Lay down the requirements of your project and if it is seen that the project needs more than one cloud, design your cloud strategy accordingly.

2. Layout a plan of action for the transfer of data between cloud servers The transfer of data from one cloud to another is a complicated, tedious and ridiculously long process. There will always be a need to transfer data amidst different cloud servers. The Multi-cloud strategy of your enterprise must address the issue of data transfer to another cloud. The strategy must include the duration of data transferring, the type of data to transfer and the concatenation of the data transfer between cloud servers. And the basic process, as always, testing the data transfer system in a trial run with test data.

3. Standardise the Multi-cloud strategy While building a Multi-cloud strategy, remember to standardise all its aspects. While working on it, design it to be compatible with Open Virtualization Format (OVF) for virtual machines, enterprise Kubernetes for containers etc. If your Multi-cloud is standardised, you can be assured of effortless cloud functioning. Also, it facilitates the process of data transfer between different clouds.

4. Obviate vendor lockin Vendor lockin is a common problem while running a cloud server. This means that your business’s data is so dependent on a single cloud provider that it becomes almost impossible to change cloud servers or shift to another one. But with a Multi-cloud environment, this problem can be averted. There is still the need to be vigilant and smart towards the integration process and cloud deployments in your system to prevent vendor lock-in. Once you build a triumphant multiple cloud strategy, your business will possess the ability of project designing with open data formats backed by every cloud environment.

5. Requirement of expertise to manage a Multicloud environment Even while using a single cloud system, the business requires a team of professionals who are learned about the methods of using it. So, if you start using a multi-cloud environment, there is an obvious increase in the need for experts to manage multiple cloud servers. If the already working professionals are potentially working with one kind of cloud, you cannot immediately shift to another completely different one before employing more experts. Every cloud is distinct and requires thorough knowledge to be employed. So, a professional versed with one cloud either needs to be trained for another one, or new experts need to be hired for the same. Multi-Cloud environments are becoming popular each day because of the increasing number of enterprises adopting this method. A Multi-cloud environment facilitates both startups and established businesses to benefit from its features. A successful Multi-cloud system requires a streamline of intensive research and the complete knowledge of the business requirements. E2E cloud solutions are designed to maintain an equilibrium between the business-specific requirements and cloud usage. For example – CPU Intensive cloud for High-performance computing, High Memory Cloud for more significant RAM size requirements, Large Disc Cloud for Data-intensive applications, and High Disc Space.

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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.

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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.

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

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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 –

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  • 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

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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

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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

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  • Network architecture used

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  • 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.

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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:

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  • 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

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So, read on to know more.

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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.

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  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.
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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.

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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

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