AI Stories
MLOps & LLMOps with Noah Gift #38
Nov 30, 2023
In this discussion, Noah Gift, MLOps leader and executive in residence at Duke University, shares insights from his 30 years of experience, including building data pipelines in the film industry. He emphasizes the crucial role of MLOps and the software engineering skills essential for data scientists. Noah contrasts Python and Rust, advocating for flexibility in choosing tools. He delves into the differences between MLOps and LLMOps, discussing security concerns and the future of deployment strategies, making a compelling case for adapting to the tech landscape.
01:11:21
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Quick takeaways
- Experience in software engineering is essential for MLOps success, emphasizing the importance of deploying models beyond mere creation.
- The film industry illustrates the importance of quality software practices and agile responses to challenges in data engineering.
Deep dives
Importance of Software Engineering Experience in MLOps
Experience as a professional software engineer is crucial for success in MLOps. Understanding the nuances of software development, such as working alongside development teams and grasping DevOps practices, is essential before transitioning to MLOps roles. Simply creating a model within a Jupyter Notebook is not sufficient; the real challenge lies in deploying that model into production systems that solve business problems and generate revenue. Therefore, aspiring MLOps professionals should first gain experience in software engineering to effectively navigate the complexities of operationalizing machine learning models.
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