The Gradient: Perspectives on AI
Shreya Shankar: Machine Learning in the Real World
Sep 7, 2023
In this episode, Shreya Shankar, a computer scientist pursuing her PhD in databases at UC Berkeley, discusses the challenges faced by machine learning engineers, including ensuring consistent performance and accuracy in production ML models. She also explores the importance of preserving information for anomaly detection, quantitative statistics for data cleaning, and the evolution from MLOps to LLMOps and FMOps. The speakers discuss the difficulties of implementing ML models and ML monitoring, as well as varying perspectives on AI as a product and its impact on customer expectations.
01:16:36
Episode guests
AI Summary
AI Chapters
Episode notes
Podcast summary created with Snipd AI
Quick takeaways
- The challenges faced by machine learning engineers include dealing with a plethora of tools, specific knowledge requirements, and the disconnect between theoretical machine learning and real-world applications.
- The emergence of generative AI with the advent of GANs sparked Shreya Shankar's interest in machine learning, and she highlights the significance of practical applications, such as using machine learning to generate music.
Deep dives
Challenges of ML Engineering and the Need for Scientific Study
The podcast episode discusses the challenges faced by machine learning engineers, such as dealing with a plethora of tools, specific knowledge requirements, and the disconnect between theoretical machine learning and real-world applications. The speaker, Shreya Shankar, explains how her experience as a machine learning engineer led her to pursue a PhD in studying MLOps. She emphasizes the importance of scientific study to address the problems that plague machine learning engineers and improve the application of machine learning in the real world.
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
