Learnings from a Machine Learning Engineer

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Towards Data Science

The article discusses the importance of considering the human aspects in machine learning projects.
It emphasizes the interaction with people involved in the project and those who use the application.
Key human interactions mentioned include communicating technical concepts, understanding end-users, and setting clear expectations.
Different groups involved in a project are highlighted, including AI/ML Engineers, MLOps team, subject matter experts, stakeholders, end-users, marketing, and leadership.
It advocates for understanding business needs and the value AI/ML brings to an organization.
The importance of effectively communicating with different audiences and creating visual presentations is touched upon.
It discusses the role of MLOps team in deploying applications and managing infrastructure.
The article stresses the need for clear documentation, communication, and formal systems to avoid miscommunication in the MLOps team.
The significance of SMEs in handling data and training material quality is highlighted.
It encourages AI/ML engineers to ask relevant questions and provide clear guidance to SMEs for improving model performance.
The article also discusses the importance of user feedback, end-user satisfaction, and model explainability in enhancing applications.

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