Why not use a Software Development Project Management Approach for ML Projects?

Using a software development project management approach for machine learning projects can present some challenges, as the nature of these projects is different from traditional software development.

Uncertainty: Machine learning projects often involve a significant degree of uncertainty, as it is not always clear what the best approach is or what the outcomes will be. This makes it difficult to plan and manage the project in the same way as a traditional software development project.

Experimentation: Machine learning projects often involve a significant amount of experimentation and iteration. This means that the project scope and requirements may change frequently, making it difficult to manage using a traditional software development approach.

Technical complexity: Machine learning models can be highly complex, and developing them requires specialized knowledge and skills. This makes it difficult to manage the project in the same way as a traditional software development project.

Evaluation: Machine learning projects require evaluating the model performance using different metrics, this can be difficult to do and measure with a traditional software development project management approach

Iterative development: Machine learning projects often involve an iterative development process, where the model is trained, evaluated, and improved upon multiple times. This is different from traditional software development, where the focus is on delivering a finished product.

That being said, many elements of software development project management can be beneficial for machine learning projects, such as using agile methodologies, having a clear project plan and goal, and having a dedicated and skilled team. But it will require adjustments to the approach and toolset to adapt to the unique characteristics of machine learning projects.

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