As part of my job description I have contributed to building end to end machine learning solutions in the form of recommender engines, Time-series forecasting models and traditional classification models in the ed-tech sector. In order to turn AI solutions to shareable data products I have gained experience in building dashboards as well as API services using frameworks like flask. I have also collaborated with the data engineering team and assisted in deployment of such solutions by using opensource containerization tools like docker.As part of a POC I have gained experience in building automated scraping scripts using python frameworks such as scrapy, in order to collect secondary data. I have also gained considerable experience in interfacing with data lake technologies like snowflake both programmatically and non-programmatically.As part of my day to day work I also got to learn how to create EC2 instances on AWS and gain remote access to it using SSH and VS code. This has allowed me to harness addtional computational resources while training large ML models. I have also acquired practical experience in using AWS sagemaker to train and deploy production grade ml models.As part of a large collaborative team, I got to use technologies like GIT version controlling tool to share production grade code across the team, as well as JIRA for agile development.