From Prototype to Production: A Successful Process for Deploying Machine Learning Models

Luigi discusses his team's successful process for taking machine learning models from development notebooks to production-ready systems that produce tens of thousands of predictions each day with minimal downtime. He highlights the importance of data validation and quality control in the deployment process and emphasizes the effectiveness of the process that his team employs.

Coffee Sessions #36 with Luigi Patruno of Machine Learning in Production 2U, Luigi in Production Part 2.

//Abstract
Learning Voraciously: We talk a lot in the community about how to learn and upskill in an efficient way. Luigi provided great insight into how he applies certain principles to his learning practices. One tip he shared is to rigorously read and digest books. Luigi himself has used books to address his knowledge gaps in areas like product, finance, etc. I appreciated the emphasis on books. A lot of the reason we feel inundated by new learning resources is that they are online. Emphasizing books, which are often far higher-quality than blog posts, can slow things down and focus our learning.

Leadership Patience: Lately, Luigi has been spending more time managing projects and the data science team at 2U. He shared a lot of his insights into how to manage data science and machine learning properly. One of the most important things he emphasized to us was his patient attitude towards solving problems important to leadership. Turning around organizations is hard work. It's slow, it takes energy, and it is a nonlinear process. As he has course-corrected at various times as a data science leader, Luigi has brought admirable patience to the task, which has helped him be more successful on the things that matter to the entire company.

//Bio

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