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Today's discussion centers on decoding the role of a data analyst and data scientist in industry settings, particularly in creating data and AI products. It emphasizes the importance of requirement analysis, data cleaning, feature engineering, and documentation. The speakers elaborate on various use cases across finance, manufacturing, and supply chains, providing a comprehensive view of tasks undertaken by different roles. A significant focus lies on how to evolve from tier one to advanced data products while ensuring reliable outcomes. Engagement in the holistic product development process is crucial for effective AI solution delivery.
Discussing various data analyst and scientist roles in product development.
Understanding requirement analysis and data cleaning processes.
Exploration of creating dashboards and data analysis by analysts.
Transitioning to AI product development and the role of data scientists.
The intricate relationship between data analysts, engineers, and scientists often blurs roles in AI development. Effective collaboration across these disciplines ensures the establishment of robust data products. For instance, aligning data cleaning and feature engineering processes directly influences predictive modeling accuracy, impacting business strategies.
AI product deployment requires not just robust models but also operational frameworks that address scalability and maintenance. The discussion highlights the importance of MLOps in ensuring AI solutions are not only developed but also operationalized effectively to meet real-world demands.
The speakers discuss how tier one and tier two data products are created to meet business needs and drive further analysis.
The discussion highlights feature engineering's role in creating meaningful metrics and classification tags for customers.
Emphasis is placed on data cleaning's importance in ensuring that AI models are trained on clean and accurate data.
The company is discussed in the context of its advancements in AI solutions within data science applications.
They are essential in the deployment of AI models and data products discussed throughout the presentation.
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