Machine Learning

We utilize deep representation learning algorithms to learn a unique representation of each student. This creates machine-understanding of student's need and capabilities. Further we utilize Text Analytics, Natural Language Processing (NLP) and Optical Charecter Recognition (OCR) to automate the process of creating & analyzing question-bank.

Application server

We run Django on Elastic Beanstalk. These servers can scale upto 4 instances based on load.

Database

Amazon RDS with PostgresSql instance is used as OLTP database. Its relational and NoSql capabilities with sharding for future scalability requirements proves to be a good fit. In addition, we plan to create dataware house for analytics.

InMemory Cache

We use AwsElastic Cache Redis as database cache for faster query response time.It also provides in memory message queues for the asynchronous tasks, such as class conduction, time-delayed messages.

Load Balancing

We have Amazon's Application Load Balancer for managing the load across multiple web servers and workers.

Background Jobs

Amazon SQS with on-demand worker instance is responsible for background tasks such as notification of today's class, report-generation, creating summary per student and sending feedback to parents.

Static Files And Media Files

We utilize Amazon S3 buckets to serve static or media files with utmost care of access permissions for private data.

Blog

For the purpose of a blog and digital marketing, we utilize Amazon EC2 instance with WordPress.