Wednesday, August 18, 2021

- PDT
The Agile AI Lifecycle: Driving Business Value through Data Science Projects
Join on Hopin
Aakanksha Joshi
Aakanksha Joshi
IBM, Advisory Data Scientist, Data Science and AI Elite
Maleeha Koul
Maleeha Koul
IBM, Data Scientist, Data Science and AI Elite

Data Science and Machine learning use cases are multi-faceted, encompassing multiple stages till adoption and productionization. Each stage comes with its own challenges and has equal importance in the overall adoption of data science use cases for business. A step-wise and agile approach to tackling data science use cases is advantageous across industries like financial services, healthcare, retail etc. The nature of use cases can be peculiar to the industry, however, a data science methodology which starts from design thinking workshops to data engineering, machine learning model building and monitoring, along with business buy-in from stakeholders is the high level state of operation. Following a structured and agile approach to build your data science use cases can help you move more of your machine learning models from silos and workbenches into production.

 

Learn more about the end to end journey of a data science use case to see how you can get the most out of your next data science project and drive value for your business.