Pandas vs Polars: Who's the fastest (library)?
MLOps, short for Operational Machine Learning, is a practice that combines DevOps principles with the development and deployment of Machine Learning models. By automating and streamlining the entire lifecycle, from data preparation to model scaling, MLOps enables organizations to effectively leverage data for real-time, large-scale ML applications. In this context, Python libraries like Pandas and Polars play a crucial role, offering powerful data processing capabilities. Comparing these libraries helps optimize MLOps workflows, ensuring efficient and successful ML deployments.