How E2E Networks Is Simplifying Cloud Computing for Startups and Enterprises

August 28, 2023

With a career defined by exceptional leadership and a profound grasp of technology, Srishti Baweja has been the driving force behind E2E Networks’ ascendancy in the cloud infrastructure landscape of India. Baweja shares how E2E Networks maintains its edge amidst the ever-evolving cloud landscape. Our proactive approach to understanding customer demands and tracking emerging trends allows us to consistently deliver cutting-edge solutions.

You can read the interview with Sugermint here

In a world where cloud computing providers abound, E2E Networks’ unique value propositions stand out, backed by an exceptional 100% ‘human’ support team, battle-tested open-source technologies, and unwavering commitment to Indian businesses’ growth. Today we will discuss the insights, innovation, and inspiration in the field of cloud computing as we explore Srishti Baweja’s transformative leadership and E2E Networks’ remarkable journey as a homegrown Swadeshi AI-First Hyperscaler.

Catalyzing Transformation with Cutting-Edge Solutions 

At the forefront of India’s cloud computing sector stands E2E Networks, a true testament to innovation’s power in the digital age. Srishti Baweja, the COO and Whole-Time Director of E2E Networks, has been instrumental in propelling the company’s rapid growth in this fiercely competitive industry.

'Our approach encompasses several key elements: firstly, providing state-of-the-art cloud solutions and highly performant infrastructure tailored for the Indian market; secondly, delivering exceptional value through optimal price-performance offerings; thirdly, building a robust platform grounded in battle-tested open source technologies, enabling seamless scaling for numerous enterprises.' - Srishti Baweja

With an unwavering commitment to adhering to Indian IT laws, E2E Networks ensures both innovation and security are at the forefront of our solutions. E2E Networks’ impact is exemplified by our pioneering move to introduce cloud advanced GPUs to the Indian market. As Baweja highlights,

'E2E Networks was one of the first cloud platforms to offer cloud advanced GPUs to the Indian market. This has enabled Indian startups and enterprises to harness the potential of generative AI and machine learning.'

This strategic leap has empowered Indian enterprises to harness the full potential of high-performance GPUs, propelling their AI and ML initiatives forward and securing a distinct competitive advantage in the AI-centric environment. E2E Networks transcends the role of a conventional cloud service provider, emerging as a dynamic force driving growth. By enabling Indian entities to adeptly maneuver through the ever-evolving technological landscape, E2E Networks catalyzes progress.

Under Baweja's visionary guidance, E2E Networks stands poised not just to adapt to change but to spearhead it in the digital era, where the boundaries of progress are limitless.

E2E Networks’ Strategy to Lead in Cloud Innovation

E2E Networks has deftly carved a path of innovation, consistently outpacing the competition and delivering cutting-edge solutions to its clients. Srishti Baweja sheds light on the company’s winning approach and its ability to remain at the forefront of technological advancements.

'We take a proactive stance in understanding our customer demand for specific technologies, and also regularly tracking emerging technologies that our customers may want to leverage. As a result, we introduce these technologies as streamlined cloud solutions, simplifying the process for our customers. This customer-centric approach keeps us ahead of the competition and helps us launch cutting-edge solutions in the Indian market,' shares Baweja.

E2E Networks’ approach is reflected in the introduction of streamlined cloud solutions that cater precisely to customer demands. Our commitment to innovation is tangibly demonstrated with the recent launch of ‘Tir,’ a revolutionary Data Science and Machine Learning platform hosted on our cloud. This platform streamlines the journey for data scientists, optimizing the process of building and deploying machine learning models.

Creating an AI-Powered Future

E2E Networks is an AI-First Hyperscaler listed on the NSE, a trailblazer offering instant access to scalable AI/ML and Cloud services. The company simplifies technology for startups, developers, enterprises, and research bodies alike.

'Our goal is to simplify the deployment, usage, and scaling of machine learning platforms, data science workflows, and various cloud applications', states Baweja.

Since 2009, E2E Networks has championed startups, propelling several towards unicorn status. An IPO oversubscribed up to 70 times in 2018, post an initial seed funding led by Blume Ventures, cemented our position as a growth catalyst.

As E2E Networks transitions to NSE Mainboard in 2022, our commitment to innovation remains unwavering. With a focus on Generative AI and machine learning, we are set to revolutionize data utilization for businesses, enabling a future of unprecedented growth.

Distinctive Factors Shaping E2E Networks’ Identity

At E2E Networks, we stand out amidst the cloud computing landscape due to a compelling array of unique features that position us as a driving force in the industry.

Optimal Value Propositions

  • We offer the best price-performance ratio within the Indian market, ensuring exceptional value for our clients.
  • Our platform relies on a battle-tested open-source foundation, trusted by our clients for seamless production operations.

Predictable Pricing and High Performance

  • With 100% predictable pricing and prepaid billing, our clients experience transparency and financial ease.
  • E2E Networks is home to cutting-edge GPUs and compute resources, empowering businesses with remarkable performance capabilities.

Empowering Developers and Enterprises

  • Our ecosystem encompasses a wide spectrum of cloud technologies necessary for reliable application development, including GPU and CPU resources, DBaaS, Object Storage, CDN, Block Storage, Containers, and more.

Human-Centric Support and Expertise

  • Exceptional 100% ‘human’ support teams guide businesses in constructing production platforms.
  • With an extensive history of enabling unicorn-scale growth on our infrastructure, we offer unmatched expertise.

Transparency, Stability, and Data Sovereignty

  • Our NSE Listing symbolizes transparency and stability, reinforcing our commitment to clients’ interests.
  • Embracing a fully Swadeshi cloud approach, we ensure businesses enjoy peace of mind concerning data sovereignty.

Data Security and Privacy

  • Prioritizing data security, we implement stringent measures to safeguard our clients’ sensitive information.
  • Through LinkedIn Live Events, regular newsletters, and targeted content, we communicate our differentiators effectively.

At E2E Networks, we go beyond offering cloud solutions; we provide an unparalleled ecosystem that empowers clients to navigate the ever-evolving technology landscape with confidence.

Elevating Security in Cloud Computing

Security takes centerstage at E2E Networks to surpass industry standards and set new benchmarks for safeguarding data. As Srishti Baweja emphasizes:

'We prioritize the utmost safety, security, and privacy of data, and this has been demonstrated by our security certifications such as PCI-DSS and ISO 27001.'

E2E Networks’ security approach operates on multiple fronts, ensuring a fortified shield around our clients’ infrastructure. Enhanced security measures include robust security groups, ingress controllers, and a strategic partnership with Bitninja, providing clients with vital anti-malware and customizable Web Application Firewall (WAF) support.

Our platform actively encourages the use of encryption, enabling clients to secure their data seamlessly through SSL for transmission. This empowers businesses to conduct operations without compromising the confidentiality of sensitive information.

As a homegrown Swadeshi AI-First Hyperscaler, E2E Networks adheres strictly to the regulations stipulated by the Indian IT Act. This commitment to compliance ensures that data remains shielded from the risks of foreign entity access, sharing, interception, or seizure, granting businesses the peace of mind and data sovereignty they deserve.

Guiding Principles for Emerging Entrepreneurs

For those embarking on the journey of entrepreneurship, Srishti Baweja, an exemplary leader, shares her insights on achieving success in the dynamic business landscape.

Focus and Direction Matter Most

In the whirlwind of possibilities, it’s crucial to maintain focus on a select few things rather than attempting to conquer all at once. Baweja emphasizes the power of a focused approach, allowing entrepreneurs to channel their energy effectively.

Patience and Persistence: The Pillars of Triumph

Success rarely happens overnight. Patience and persistence, as Baweja affirms, are integral to the journey. Embracing setbacks and challenges with unwavering determination paves the way for long-term success.

Innovation and Timeliness Propel Growth

Staying innovative is a cornerstone of success. Baweja’s advice is to innovate and deliver within stipulated timelines, ensuring that entrepreneurs not only stay relevant but also consistently exceed expectations.

Customer Engagement: A Non-Negotiable Factor

Engaging with customers is a key differentiator. Baweja stresses the importance of building strong customer relationships, understanding their needs, and tailoring offerings to exceed expectations.

Your Successful Cloud Journey Starts Here

Explore a world of possibilities within our diverse cloud ecosystem. From cutting-edge GPUs and compute resources to DBaaS, Object Storage, CDN, Containers, and more, we provide the tools you need for innovation. Experience exceptional value, top-tier support, data sovereignty, and AI-first approach.

Reach out to us or schedule a free trial to see us in action.

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

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

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

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

Reference Links

Build on the most powerful infrastructure cloud

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