[ Ø ] Harsh Prakash

Quiet Musings on Cloud, Machine Learning, Big Data, Health, Disaster, et al.

About

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Hi, I am Harsh. I am a dad.

I work as a certified management consultant (AWS, Agilist, PMP, GISP), and a senior Cloud architect in the Washington DC Metro; implementing large-scale Cloud and Machine Learning solutions for clients, making sure they work as desired and with each other.



I have been in the tech industry for nearly two decades helping federal customers where I implemented and managed end-to-end enterprise-class Business Intelligence (BI) solutions and their integration. My experience is with the U.S. federal government, project management and Cloud as it relates to Artificial Intelligence; aid; fraud, waste and abuse; health and research; and hazard, floodplain and land-use. My strengths are in implementing and balancing Commercial Off-The-Shelf (COTS) software with open source software, and having expertise in the full stack to create an end-to-end solution.

A graduate of the University of Virginia (UVA) where I was a member of the Department of Computer Science Web Team. Previously, I launched websites to link technology professionals with volunteers, and was elected as Chair of the American Planning Association’s (APA) Technology Division. I also volunteer on the Board of Directors at Global MapAid (GMA) where my focus is on leveraging emerging technologies for aid work, and on fundraising to expand the USA charity and volunteerism to help global NEET youth (Not in Employment, Education or Training youth). In 2020, we began volunteering on an irrigation borehole project to model optimal drilling locations (MODL) using Artificial Intelligence and Machine Learning with our partners at the Water Technology Institute, Ethiopia, and George Mason University, USA.

For consulting opportunities, you can contact me on the social media below, or support here

GitHub, GitLab, kaggle, Twitter, SlideShare, LinkedIn (AI/ML Lunch-&-Learn Group, APA Technology Group), Instagram, Facebook

▲ Model Optimal Drilling Location (MODL)

▲ NASA Data Science Day Plenary: Applied Machine Learning (ML)

▲ Making Cloud Procurement Easy with AWS Marketplace, Automation, and Governance