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Packaging MLOps Tech Neatly for Engineers and Non-engineers // Jukka Remes // #322

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Navigating MLOps Tools in Machine Learning Research

This chapter discusses the versatility and practicality of MLOps tools like Kubeflow, MLflow, and KServe in supporting diverse machine learning models and use cases. It highlights challenges in transitioning research to industry applications, emphasizing collaboration and reproducibility. The conversation also covers the importance of creating user-friendly platforms and pipelines that streamline the research process and foster innovation in machine learning applications.

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