Dr. Muhammad Sheikh Azam
SKF Group, Sweden
Understanding the Question Marks on the Success Rates of Data Science Projects: Lessons Learned from Academia and Industry.
Within the domains of algorithms, optimization, applied artificial intelligence (AI) and machine learning (ML), Dr. Muhammad Azam Sheikh has actively contributed for several years under various roles and capacities such as research projects supervision and evaluation, curriculum, and project handbook development, teaching and conducting both theoretical as well as applied research in collaboration with industry.
At SKF, besides working as a lead data scientist and business analyst, Azam is also developing the playbook for delivery readiness, evaluation, and follow-up process for data products. The focus is to mitigate challenges faced during the entire lifecycle of AI/ML projects using real-life data science use cases from the manufacturing process. The aim is to set the data science projects for success right from the ideation stage.
Before joining SKF, Azam previously worked as Data Science Research Engineer at Chalmers University of Technology, Gothenburg Sweden. The role was connected to the Big Data initiative at Chalmers and Chalmers AI Research Centre (CHAIR), where Azam has been the primary applied data science, resource person, for a number of projects within the areas of ICT, Production, Health Engineering, Energy, and Transport.
Today, data science has left its footprint in almost all fields of life. There are a lot of applications and systems around us where the latest developments in the field of data science have resulted in remarkable results. However, an important aspect is to study, how many “data science stories” are concluded with a happy ending?
In this talk, I’ll highlight
- What are the success rates of data science initiatives?
- Why do certain projects fail to deliver anticipated value?
- What are the important factors and best practices that must be considered to set up a data science project for success?
I’ll be reflecting on personal experiences both from academia as well as industry. The talk will cover aspects for the general audience who want to learn how to set realistic expectations out of a data science engagement. However, the main targets are graduate and post-graduate students and researchers who are pursuing a career as applied data scientists.