Agile project management is an iterative approach to planning and managing a project. In other project management approaches to software development, such as the waterfall model, you typically identify all requirements in advance and meticulously plan a project from start to finish.
In contrast, agile project management does not set the project plan in stone. Instead, one breaks the project down into parts and sets goals that can be achieved in small stages, which in turn can take between one day and several weeks. In this way, the project is developed step by step and can be tested and adjusted frequently to identify any new requirements.
Agile project management has proven to be an extremely useful approach in the implementation of Machine Learning projects. The main advantage of agile project management is that it is very flexible and can therefore accommodate unforeseen changes, risks or problems that may arise during a project. This is the right set-up for machine learning projects where - unlike projects in other fields - uncertainties are inherent. There are many things that you don't know in advance.
For example, the answers to the following questions: How good is the Data quality? Can the process be predicted with this data? Are the models mature enough to meet their objectives? With agile project management, answers to these unforeseen questions can be answered and other problems can be solved more easily.