AWS To Develop More AI And ML Based Public Services In India

  • Amazon Web Services (AWS) has entered into public sector partnership with several Indian government agencies and ministries.
  • The government of Maharashtra has been initially chosen for providing e-services at the village level.

Amazon Web Services (AWS), the world’s largest provider of cloud computing platform has decided to enter into public sector partnership in India. Through several important agencies and ministries in India, it is expecting to develop public services in India with the help of Artificial Intelligence (AI) and machine learning (ML) tools.  

Speaking to the, Manav Sehgal, head of solutions architecture at Amazon Web Services, said, “From the common service centre to the Digital India mission, the Skilling mission, the Smart City mission, they are increasingly adopting AWS Cloud and partnering with us. So India public sector is actually building on AWS cloud as we speak,”

AWS has chosen Maharashtra government for developing mission-critical applications and databases such as, the Farmer Loan Waiver System, the Maharashtra Real Estate Regulatory Authority and the Direct Benefit Transfer. All of  these incorporate AI and ML. 

In collaboration with AWS’ ML tool, the government of Maharashtra has set up the Common Services Centres for providing e-services at the village level.  

Textract service

AWS’ recently launched Textract service makes use of AI tools and could make digitisation easier at the Common Service Centres (CSCs). Textract is an AI-based tool that analyses digitally printed forms and scanned documents and then extracts text from them – all in real-time. This hugely assists villagers in doing a digital entry at the CSCs and thus speed up the entire digitisation process.


Open datasets can help the public sector in building specific applications. However, the lack of these in India is a big challenge for creating AI and ML services

Manav Sehgal says, “You do have But if you go and search within, and you will most likely find data sets, which are a summary of a dashboard. The raw data needed for machine learning is missing.”

Despite such challenges, it is hoped that datasets can be obtained in a better way to create ML applications based upon it. “We are in talks with a number of other public institutions and academia to bring in their data sets, possibly even Ministry of Urban Affairs. They could be certain city level data, which can be desensitised for instance,” said Manav Sehgal.