Abdullah Khan Ghori

Precocious and ready to accept increasingly complex challenges associated with maintaining and exploiting growing data stores. Driven to expand experience through hands-on training and guided participation in effective data management tasks.

Ready to immediately contribute beneficial input to employers. To seek and maintain full-time position that offers professional challenges utilizing interpersonal skills, excellent time management and problem-solving skills.

Enthusiastic and eager to contribute to team success through hard work, attention to detail and excellent organizational skills.

• Used Microsoft Excel to develop inventory tracking spreadsheets.
• Deployed Static website on AWS
• Deployed Windows Virtual Machine instances using EC2.
• Planned migration of existing IRS website to AWS with final architecture model to be implemented and steps of migration.
• Deployed CRS instances on AWS using AWS Personalise.

Open Source prototyping using Arduino

SDLC Documentation ( Snapdeal )

A project report submitted in partial fulfillment of the requirements for the award of the Degree in the form of a bonafide work.

Report titled "Snapdeal through the eyes of an Engineer" gives an overview of the Software Development Life Cycle using Snapdeal as a case study.

Mini Project ( Deploying a Micro-blogging website )

A project report submitted in partial fulfillment of the requirements for the award of the Degree in the form of a bonafide work.

Report titled "Ecrivians: Paving way for the blogging community" gives an overview of the Implementation Infrastructure comprising of HTML, CSS and JavaScript code implemented on Apache Tomcat 9.0 Web Server, followed by a live implementation and demonstration of the website.

Major Project ( Smart Agriculture using TinyML-managed system )

The aim of this live project is to predict suitable crop based on the given climate parameters and geographical location. This is done by taking live data input stream over IoT network via sensors on Arduino Boards. Data from sensors is stored on AWS S3 Bucket instances and used to train CNN model on AWS EC2 instances. The trained model is then deployed on Arduino Boards using TensorFlow Lite to predict the crop based on the season and area of the field using stored datasets or live data streams.