Agile, products and changes in the world of AI
When agile came into being, a lot of the new technologies that we know today did not even exist. Does our definition of agile and the process that we follow change with new areas of technology thinking like data science, artificial intelligence & big data engineering coming into picture? Does INVEST make sense for data stories? Do we even write stories or should we work hypotheses instead? Do we need Acceptance Criteria or Exit criteria? How do lean, agile & product principles work in context of AI problems? What happens when science meets products?
Outline/Structure of the Talk
- How are data projects different from regular delivery projects?
- Pre-requisite: mindset to experiment
- Importance of outcomes and metrics in a data project
- Practices - Division of labour, Estimations, Interaction with other roles in a team
- Team setup - Important roles
- What are the useful tools and frameworks?
- How to think about data roadmaps?
- Understanding of need for experimentation
- Thinking frameworks for:
- Evolving roadmaps
- Doing incremental science based exploration to solve business problems
- Full project lifecycle of a "Data" centric product
Product managers, Business Analysts, Project Managers, Data Scientists, Data engineers, Tech Leads, Engineering Managers, Delivery managers
schedule Submitted 4 years ago
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