Software Engineer focused on DevOps, Machine Learning and Full Stack Development. Basically, everything ;).
|ML Libraries/Tools||TensorFlow, NumPy, NLTK, SciKit Learn, Pandas|
|Web Technologies||Angular, React, Node.js, Next.js, Flask, REST API, GraphQL|
|Database||MongoDB, Cassandra, MySQL, PostgreSQL, Redis|
|Software/DevOps Tools||Kubernetes, Docker, Openshift, Istio, Ansible, Travis CI, GitHub, Jenkins, Apache Kafka|
|Cloud Services||IBM Cloud, Amazon Web Services, Microsoft Azure, DigitalOcean|
We developed an efficient model for storing and retrieving files over the cloud using a data-centric approach. This management of files promises a better access speed (upload and download) from the local machine to the cloud and vice versa.
This paper demonstrates an algorithm that can be applied to both black box and white box testing to get some of the best test cases rather than selecting all the parts using a Genetic Algorithm.
This paper explains different advancements, benefits, and injury caused or can be created to the society which is also our principal contribution towards the subject. It also tells why the future of Artificial Intelligence, despite having advantages, remains uncertain.
This paper shows the advancements done in the field of Data Analytics with Cloud Computing and Big Data, and also proposes a scheme for making Big Data Analytics more accurate, efficient and beneficial to the cloud environment.
With the evolution of different software development models over the past years, it became a topic of utmost interest to categorize and segregate them depending upon the applications, advantages, and disadvantages. This paper discusses various models on different metrics with the pros and cons of each of them and also helps us select an appropriate model depending on the project.
This paper proposes a scheme for making Big Data Analytics more accurate, efficient, and beneficial. It also includes MapReduce Algorithm which will help in maintaining a log of user’s activities in the cloud and show the frequently used services.
This paper proposes a dynamic load balancing algorithm that will assign load to the servers depending on the type of content it possesses.