Agile Processes

Agile processes include various methodologies under which requirements and solutions evolve through the collaborative effort of cross-functional teams and their clients. It advocates methods such as:

  • adaptive planning

  • evolutionary development

  • early delivery

  • continual improvement

  • rapid and flexible response to change

  • Vision: general direction, goals, aspects

  • User Stories: descriptions of use cases and benefits

  • Sprint Planning: planning for work items for sprint periods, usually 2-4 weeks

  • Daily Standup Meetings: team members provide a quick summary of what was accomplished in the past day, what is planned for the current day and any work blockers that exist

  • Sprint Work Progress: team members work on their assigned items

  • Retrospective and Sprint Review: team members provide feedback

  • Work Products: work results are produced and demonstrated at the end of the sprint

Agile Fit with Machine Learning

As detailed below, there are aspects of Agile processes that fit particularly well with Machine Learning.

Cross Functional Teams

The full scope of Machine Learning processes involves a number of functions. Cross functional Agile teams include expertise from needed areas.

Variety of Clients

Machine Learning typically serves a number of client organizations. Cross functional Agile teams typically include representatives from client organizations.

Dynamic Data Sources

Agile teams can respond quickly to rapidly changing data.

Evolving Technologies

Agile teams can continually adjust to rapidly evolving Machine Learning technologies.

Sharing Across Organizations

Cross functional teams promote the sharing of data, information and knowledge.

Continuous Learning

Agile processes promote continuous learning and improvement.

Minimum Viable Product and Pilot Projects

Minimum Viable Product (MVP) and Pilot Projects allow enterprises to test ML/AI applications at a relatively low level of investment.

References