
Noah Gift
Founder of Pragmatic AI Labs, devises and teaches data science curricula at prestigious American universities, and has authored eight books on data science and related topics.
Top 3 podcasts with Noah Gift
Ranked by the Snipd community

7 snips
Nov 30, 2023 • 1h 11min
MLOps & LLMOps with Noah Gift #38
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.

May 4, 2021 • 1h 17min
467: High-Impact Data Science Made Easy
Noah Gift, founder of Pragmatic AI Labs and author of eight books on data science, shares insights on the transformative power of data science. He discusses educational options beyond traditional universities, emphasizing hands-on experience. Noah highlights urgent technology applications and the democratization of machine learning through tools like AutoML. He also explores diversifying income streams and the importance of leveraging free time for impactful problem-solving. His Coursera course aims to make advanced concepts accessible to all.

Sep 20, 2021 • 50min
#71 Scaling Machine Learning Adoption: A Pragmatic Approach
In this engaging conversation with Noah Gift, founder of Pragmatic AI Labs and an expert in cloud computing, he shares insights on operationalizing machine learning within organizations. He discusses the shift from theoretical to practical applications, emphasizing hands-on experience and effective communication. Noah compares data science to Brazilian jiu-jitsu, promoting a results-oriented approach. He also dives into the importance of choosing the right cloud tools and balancing specialization with generalization for future success in the evolving landscape of data science.