Machine Learning Systems in Industry: Challenges and Practices

This report explores the various challenges and practices involved in deploying machine learning (ML) systems within the industry. It systematically covers various stages of the ML deployment workflow, including data management, model learning, testing, deployment, and cross-cutting aspects such as optimization and end-user trust. It emphasizes the importance of a holistic approach that balances technical excellence with ethical considerations. Moreover, the report also provides a deeper understanding of the complexities involved in developing robust, scalable, and secure ML systems, providing a foundation for future research and innovation in ML deployment practices.

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