This was a private project. I cannot share many things about it but I will share what I contributed to this project.

Gather data from different sources(APIs, Cloud, Databases).

Collaborate with engineering teams and stakeholders.

Performed data wrangling (merging, cleaning, preprocessing, feature engineering), data correction and statistical data analysis.

Experiment and build machine learning and deep learning models.

Perform experimentation and feature engineering to improve model accuracy.

Analyze data and get meaningful information and patterns.

Create Python and bash scripts for data gathering and other repeated processes.

Handle all machine learning operations(Mlops with architecture and implementation)

Write Mlops services in Python from scratch for production environments.

Dockerize Mlops services and deploy them on client sites.

Visit client sites and deploy on-premise complete machine learning solutions.

Use different cloud services daily (aws ec2 Linux GPU instance, lambda, s3, sam, code- commit(git) and cloud watch/event bridges etc.) for data fetching, preprocessing, wrangling, storing, modelling and deployment.