Exploring the integration of Generative AI into existing Machine Learning pipelines reveals promising growth prospects alongside inherent complexities. Challenges such as data disparities, interpretability issues, ethical dilemmas, and regulatory obstacles emerge, underscoring the nuanced landscape of deploying Generative AI within ML frameworks. This exploration dives deep into these multifaceted aspects, highlighting critical hurdles and offering essential best practices for successful implementation.

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