An Agile Approach to Machine Learning Project Implementations
Agile Software is becoming a very common implementation in multiple organizations currently since the solutions with agile implementation evolve through collaboration between self-organizing, cross-functional teams. To implement a machine learning project, there are three hurdles that need to be overcome—an appropriate accurate model, uncertainty and the lack of in-house capability.
The Agility project management has a framework, which gives it the ability to handle highly unpredictable projects like machine learning. One should start with small investments as the license costs for any AI solution is extremely high in cost. After doing the proof of value and finally demonstrating that the chosen AI will be apt for the business, one should shift to agile driven contracting that focuses on the delivery of value and not just the solution.
The retrospective is another aspect of agile, which is very strong. It is better to dedicate time to brainstorm the way the work is done rather than the work itself. This also implies adapting the methodology as according to the needs of the team. With heterogeneous teams, agile tends to become very complicated. The more diverse the team, the more important is to have a non-judgmental retrospective.
From the basic research, the sole purpose is to generate new knowledge and sometimes the value of that new knowledge in some cases is not legitimate. To conduct a research with a lack of business goals is a sure road to failure. There will be no clear outcome and no way to commit to anything and no proper way to proceed. Very few organizations can afford an arbitrary enlightenment of a business model and the only outcome from a non-applicative research is an unexpected enlightenment.