Operationalizing AI at speed and scale for COVID and Beyond

Date: 10.16.2020 | HITC Editor

LIVE PLENARY WEBINAR | Thursday, October 8th | 12:00-1:00 EDT
Register to Attend | Add to Calendar

Operationalizing AI at speed and scale for COVID and Beyond

Dr. Suman De – Head of Government Healthcare Analytics, Infosys Public Services
Idris Motiwala – Principal Product Manager, Couchbase
Dr. Daniel Parton – Lead Data Scientist, Bardess Group, Advisor, Tangent Works

Public sector organizations are turning towards artificial intelligence (AI)/machine learning (ML) models to make more informed decisions across areas like re-opening offices and servicing citizens. However, they are struggling to build and operationalize these models quickly. Various studies have found that organizations take months to develop machine learning models and that most of these models (~95%) never making it through the very complex and time-consuming process.

Already constrained for data-science resources, how can public sector organizations build, train and deploy effective machine learning models, across program areas, quickly and arm policymakers with timely insights in an environment where real-world data is changing rapidly?

An automated data science approach can address these challenges and enable public sector organizations to operationalize AI/ML at speed and scale. From democratizing data science skillsets (i.e. making it easier for anyone to build ML models) to accelerating the model deployment lifecycle, this approach can help public sector organizations effectively use AI to improve program outcomes and improve citizen experience.

To learn more, join our panel featuring subject matter experts and public officials who will discuss:

1. The challenges in operationalizing AI/ML and how to address them with an automated data science approach
2. How to accelerate the AI/ML development cycle – from identification and ingestion of data to model deployment, and automate time-consuming processes like data analysis, feature engineering etc.
3. How to build new and repeatable AI/Machine Learning models within minutes to support different program areas like COVID-19 management and return to work
4. How to scale AI/ML capabilities to enable faster decision making in the COVID era and beyond

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