Why E2E Cloud Platform Emerged as the Choice for Clovia?

February 22, 2018

About Company: CLOVIA is a full stack lingerie brand backed by Ivy Cap Ventures, addressing India’s underserved women’s innerwear and sleepwear market. Clovia product offerings comprise a wide variety of bras, panties, nightwear, camisoles & shapewear. The company sells its products through its own e-store clovia.com and other partner websites. The brand delivers products in more than 970 cities and serves over 10000 pin codes locations. The website currently serves monthly traffic of over 8.7 lakh users with over 40.9 Million-page views. This article illustrates the benefits that are associated with Cloud Infrastructure migration from any other public cloud provider to E2E Networks. The results directly encompass the benefits of significant cost reductions and better performance associated with E2E Networks Infrastructure.

E2E NETWORKS successfully migrated clovia.com from AWS to E2E Networks with a Cloud Agnostic approach.

Client Requirements:

  • Increase website performance with reduced cost
  • Infrastructure management and efficient utilization of resources
  • Performance of stacks & database queries equivalent or better than existing services
  • A holistic solution that would accelerate performance, condense resources and implement best-practices

The Challenge:

The client’s portfolio involved many products and the functions of the IT Admins & DevOps team were overloaded with work comprising repetitive problems. Just like many other companies, the client’s focus also to cut down their existing cloud Infrastructure and operational costs while delivering best-in-class service to its’ customers.

E2E’s Prescriptive Approach

  • Migration of CDN from AWS CloudFront to E2E Networks based CDN setup.
  • Setup MySQL Database from AWS-RDS to E2E Networks based HA-DRBD MySQL
  • Migration of Mongo node from SPOF to HA-Mongo replica set cluster
  • Setup Distributed Memcached system with 4 services for users and sessions management
  • Move single Redis and RabbitMQ to cluster setup
  • Transform web stack from Apache + mod_wsgi to Nginx with Gunicorn + supervisord.

The Solution

Strategy Overview-

Phase 1 – Preparing right-sized infrastructure resources to migrate Clovia to E2E Networks. We had to ensure that every component used here should be Highly Available. Phase 2 – Testing components functionality and working on performance optimization strategies to reduce lag by tweaking server specification and tuning software stack configuration.

The Execution –

Before we started working on it, Clovia’s AWS architecture involved a Single Point of Failure with recurrence load issues on the server during peak hours. We monitored the website and server performance while on AWS for a few day before performing any activities. During this phase, we audited and analyzed the right-sized setup requirements.

Phase 1: High Availability setup

We initiated High Availability setup with E2E NETWORKS infrastructure CDN by using PCS cluster technology with Nginx instances acting as a CDN machine serving from origin like S3 buckets. With respect to this, we worked on setup involving a HAProxy Load balancer with a set of instances using PCS web servers configured with Nginx as frontend with proxy pass Gunicorn + supervisor as the application was based on Django stack.

  • Additionally, for MySQL Database we selected PCS with DRBD technology possessing read replica as an alternative to RDS
  • For NoSQL Mongo Database we used a replica set cluster with a set of nodes with Arbiter to avoid split brain
  • To replace elastic cache, we deployed distributed Memcached system with 4 different processes split into two processes each handled by a server to manage page cache
  • To maintain user session, we used Redis Master/Slave setup with sentinel mechanism.
  • For message queuing we used RabbitMQ cluster with mirror queuing setup, an engine we use for searches in sites driven by solr engine
  • Not forgetting the security aspects, we implemented a setup on separate VLAN by deploying zentyal firewall and NAT- cluster. This enabled secure server access and avoided Network obstruction.

Finally, we shared this new setup and initially, sync’d code & database to perform load testing and benchmarking.

Phase 2 – Benchmarking

All validation, user testing, performance and security tests were performed as per the stages set in the pre-production environment so that everything should be ready for the final sync of the code, database & DNS switchover on the final day. During our benchmarking sessions, we were challenged by the RDS query execution time, which took around 0.6 – 0.7 sec. as compared to our tuned HA-DRBD MySQL setup with high specification server which took around 1.5 sec. The problem identification consumed a lot of time and energy. We deep dived into a lot of parameters such as MySQL changes, hardware configuration etc. to identify the root cause. Finally, we narrowed down the problem to the disk IOPS availability. To rectify the problem, we re-flashed the servers with RAID 10 disk configuration as compared to the previous RAID 1 configuration which brought down query execution time from previously 1.5 sec. to now 0.4- 0.5 sec, even better than RDS.

The Results

Clovia was impressed with service and support they received from E2E Networks

“We chose E2E for their competitive prices when compared to AWS. This was technically a winning choice for Clovia as E2E provides better machines, configuration and support as compared to AWS. E2E uses APM tool – Instana which in turn increased Clovia’s performance significantly. Their 24X7 support commitment and prompt ticket resolutions is commendable. E2E team’s skill set has enabled Clovia to grow with agility. We highly recommend E2E for it’s cost effectiveness and infrastructure security specially for fast moving start-ups.

-Clovia”

Following a fully operational production environment on E2E Networks, we helped Clovia to enhance and optimize their capabilities. The website is presently serving from E2E Networks infrastructure and our client is more than happy with the with the value that we provided. Finally, clovia.com was live on E2E Networks infrastructure. Following a fully operational production environment on E2E Networks, we helped Clovia to enhance and optimize their capabilities. The website is currently serving from E2E Networks infrastructure with excellent results. Our infrastructure offered significant performance & operational enhancements

– Cheaper – Clovia witnessed more than 48% savings after migrating to E2E Networks Infrastructure.

Faster – The website currently serves 2X traffic smoothly with better CDN performance and less query execution time as compared to the initial stages which have brought additional savings to the client.

Better – E2E Networks redundant & Highly Available infrastructure easily handles everything with better performance.

More Resources – They didn’t have a staging environment on AWS but we provided them with a 4 node staging setup which made their testing and deployments slicker. Fostering growth & innovation nowadays, the site experiences an increase in the traffic: there is no fluctuation in server resources, E2E Networks servers easily handle traffic with an average monthly visit of 8.7 lakh users & over 40.9 Million-page views. E2E Networks believes that the information in this document is accurate as of its publication date; such information is subject to change without notice. E2E Networks acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this blog.

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Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
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Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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