Practical AI
Machine learning at small organizations
Jan 17, 2023
Kirsten Lum, Co-founder and CPO of Storytellers AI, shares her insights on the unique challenges small organizations face in implementing machine learning. She highlights that underutilization often stems from mismanagement, not a lack of data. Kirsten discusses the importance of adaptable data scientists and effective project management in driving successful ML practices. Additionally, she emphasizes the shift towards valuing real-world outcomes over mere model accuracy, advocating for a cultural change in data-driven decision-making.
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Quick takeaways
- In small organizations, data scientists need to have a deep understanding of the entire machine learning workflow, from data infrastructure to model deployment, allowing them to gain hands-on experience and be involved in the end-to-end process of data science.
- Working as a data scientist in a small organization provides the opportunity to try different tasks and roles, such as project management and MLOps, and have a wider scope of responsibilities compared to data scientists in large companies.
Deep dives
The Role of a Data Scientist in a Small Organization
In a small organization, the role of a data scientist is not just about training models, but about converting data into business value using data science techniques. The data scientist needs to have a working knowledge of the entire machine learning workflow, including data infrastructure, feature engineering, training models, deploying models, and monitoring their impact. The advantage of being a data scientist in a small organization is that you can have a broader and hands-on experience, working on various tasks and roles across the entire workflow. This allows you to gain a deeper understanding of different components and be involved in the end-to-end process of data science.
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