Building an Agile AI Project Management approach

In AI projects, as in many other types of projects, we follow an agile process in the management of projects. A typical project includes at least one Data Scientist and one Software Engineer. These usually are supported by DevOps and Machine Learning Engineers.

A project manager is often supported by a technical lead that oversees the processes to make sure they follow the best Machine Learning/AI and Software Engineering practices in any project and ensure alignment with the client's expectations.

Building any AI solution is not a deterministic and unidirectional process, it requires iterations and refinements of the process.

Several steps such as Exploratory Data Analysis (EDA), Data Processing, Model Selection and Validation, which are part of the process, need to be refined to align with the Business strategy.

Due to this non-deterministic nature of AI projects, different methodologies, such as Cross-industry standard process for data mining (CRISP-DM) and Team Data Science Process (TDSP), have been suggested to keep the team and stakeholders aware of the different processes until a final solution is reached.

Fig 1: TDSP methodology

Learn more about TDSP:

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