Top 9 Reasons why your Start-up/SME should choose Cloud Technology

August 21, 2018

“Line-of-business leaders everywhere are bypassing IT departments to get applications from the cloud (also known as software as a service, or SaaS) and paying for them like they would a magazine subscription. And when the service is no longer required, they can cancel that subscription with no equipment left unused in the corner.” ~ Daryl Plummer, Managing Vice President and Distinguished Analyst at GartnerRoughly 5 out of 6 large businesses list cloud computing as one of their top innovation priorities.A report by McKinsey states that IT leaders appear to be less satisfied with their business ability to innovate & want to take it to cloud computing.

As you start your voyage, here are a top 9 reasons why Cloud Technology is a smart move right from the beginning

  1. Cost
  2. For startups/SMEs the traditional hardware proves expensive & ties up a lot of capital — both in terms of procurement and maintenance. Switching to the cloud provides better service and computing choices to the business thus offering a value for money compute model.
  3. Time to Market
  4. Cloud speed-up the application development and overall processes. Business can provision servers quickly and flexibly to meet the demands. Another important factor is quality which is a clear benefit for businesses switching to cloud computing.
  5. Integration
  6. Cloud solutions offer simplicity by helping the businesses to integrate their on-premise infrastructure with cloud or run their complete operations on the cloud. Cloud computing subscriptions, IaaS, PaaS services create a more manageable, cost-effective solution for startups & SMEs.
  7. Scalability
  8. The ability to scale or to quickly provision a server during high-demand situations certainly drops the business barriers. This is adorable, cloud technology is a business enabler, rather than just a bunch of gimmick tools.
  9. Performance
  10. Cloud can rapidly accelerate your efficiency. The businesses can experience significant performance improvements. From efficiency gains to improved processes, cloud computing can pace the progress while avoiding big-capital expenses.
  11. Accessibility
  12. The trend in computing started with a single device followed by some dumb terminals. It shifted to computers with a network and now it’s all about network-based computing through Wi-Fi, Mobile data or expanded networks. The cloud is the key to freedom, we are no longer tied to a single server.
  13. Reliability
  14. The overall reliability is an important metric by which a technology might fall or rise. The situations like server outage, crash, failure can happen to any Start-up/SME. This can harm your clients, employees, cost as well as hard-earned reputation earned over a period of time. The cloud reliability model aims to let users access their data and files at all times. This ensures a reliable, safe and fail-proof system all the time.
  15. Security
  16. And that’s a respectable feature. For every type of Start-up/SME business the cloud security is like getting a booster shot of enterprise-level security.. It’s a situation similar to ‘’herd immunity’’ where every application or service is immunised against security attacks. This will also help in making a better ecosystem of services and apps, meaning more demanding and subsequently better options for all the Start-ups/SMEs
  17. Simplified Model
  18. Cloud computing world allows start-ups/SMEs to minimize the cost of executing an idea. If it flops, they’re not left ruined. That lets business person cycle through innovative ideas more rapidly, trying out their ideas with right-set of compute and finally hit a sweet spot when they are successful

What should start-ups or SME business consider before moving to the cloud?

While the above benefits are easily achievable migrating to the cloud, you shouldn’t rush to migrate instantly. It is recommended to do your research and identify a provider with service and support to match your requirements.Most start-ups and entrepreneurs in India go with the likes of big computing players as their primary choice BUT being attached with these global giants would hurt in terms of cost, privacy laws, price to performance ratio, USD denominated prices, vendor lock-ins and more which would be visiblenot in a short span but during the long run. Moreover, when you want to switch to another provider it would be difficult to migrate as you would have tied completely to your provider. Therefore, a due diligence is a must before choosing a cloud provider.E2E Networks is an Indian Cloud Provider & believes in the philosophy of “We Do It For You”. At E2E, we have vast experience in implementing and managing infrastructure for the web, mobile, or enterprise-centric workloads. Our latest generationCloud InfrastructureandCloudOpsservices can help you change the way your organization works. Our provenCloud Agnosticplatform has helped and served 100’s of start-ups and businesses in India to close the technological gaps that they encountered during their journey. A smart approach can yield great results that build momentum to get your business off the ground.Experience the difference with E2E on the #Cloud and get #FutureReady.Don’t wait! Get on a call with our Cloud Computing Specialists on +91-11-3001-8095 to get your customized solutions designed or drop us an email atsales@e2enetworks.comwith just your name, email or contact number, our team will contact you to help & quickly get started.]]>

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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.

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

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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?

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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 –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
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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

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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

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.

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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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  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.

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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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