The Agile AI Lifecycle: Driving Business Value through Data Science Projects

Aakanksha Joshi
IBM, Advisory Data Scientist, Data Science and AI Elite

Aakanksha is an Advisory Data Scientist in the Data Science and AI Elite team at IBM. Based out of New York, she works with clients to build data science and machine learning driven solutions and achieve business impact. Her background includes a Master’s in Data Science from Columbia University and Bachelor’s in Computer Science from University of Delhi. Her work spans across industries and applications of machine learning - she has led and delivered projects in Financial Services, Automotive and Supply Chain among others. She is also a strong advocate for using AI for Social Good.

Maleeha Koul
IBM, Data Scientist, Data Science and AI Elite

Maleeha works with clients across industries to help their businesses in harnessing the power of data using AI and machine learning. She holds a Masters's degree in Computer Science with a specialization in Data Science from the University of Texas at Dallas. She is passionate about building AI workflows for organizations to have a business impact. Maleeha has experience with clients in Healthcare among other industries to help them solve their top business use cases and bring them to production. She is an advocate of trustworthy AI in the healthcare space and was recognized with the Data and AI Hero award for her efforts in transforming healthcare using AI for a North American healthcare company. She is an admirer of nature and loves spending time outdoors with friends and family.

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.