Using Agile in Machine Learning projects comes with its own set of challenges. We need a flexible and adaptive approach that considers the unique challenges of data-driven development. Blending Agile with data-focused methodologies, adopting frameworks like CPMAI or Microsoft's Team Data Science Process or Data Driven Scrum (DDS), and fostering increased collaboration between the datascience and softwaredevelopment teams are key factors for success. The key is to leverage the strengths of Agile while tailoring it to meet the specific demands of AI-ML projects.

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