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?

 
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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?

Learning Outcome

  • Understanding of need for experimentation
  • Thinking frameworks for:
    • Experiments
    • Evolving roadmaps
    • Doing incremental science based exploration to solve business problems
    • Full project lifecycle of a "Data" centric product

Target Audience

Product managers, Business Analysts, Project Managers, Data Scientists, Data engineers, Tech Leads, Engineering Managers, Delivery managers

schedule Submitted 7 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Tathagat Varma
    By Tathagat Varma  ~  6 months ago
    reply Reply

    The topic is relevant but your proposal doesn't share a lot of depth or details about the same. Can you share a strawman deck and also a past video from your talks?

    • Richa Trivedi
      By Richa Trivedi  ~  6 months ago
      reply Reply

      Hi Tathagat
      We'll add the details shortly.

      The video from a previous talk on a similar topic is attached in the links section of the proposal.


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