Artificial intelligence (AI) and machine learning (ML) projects require data scientists because the focus of their work is on understanding and leveraging data to train models, as opposed to software engineers, whose focus is on developing and implementing software systems. Data scientists are experts in statistics, mathematics, and computer science, and they have the skills and knowledge to work with large and complex data sets, extract insights from them, and use that data to train and optimize machine learning models.
In contrast, software engineers focus on the design, development, and maintenance of software systems. They have expertise in programming languages and software development methodologies, and they use that knowledge to create and maintain software applications.
Data scientists are responsible for collecting and cleaning data, selecting appropriate algorithms and models, and fine-tuning their parameters to optimize their performance. They also interpret the results and communicate them to stakeholders to help them make better decisions.
On the other hand, software engineers focus on implementing the models and algorithms developed by data scientists into a production-ready software. They work on building the infrastructure to train, serve, and monitor the model, as well as integrating it with other systems.
In summary, data scientists are responsible for understanding and leveraging data to train models, while software engineers are responsible for developing and implementing software systems. Both roles are important in AI and ML projects, but they have different focuses and require different skills and expertise.
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