The Gradient: Perspectives on AI Shreya Shankar: Machine Learning in the Real World
6 snips
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.
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10
Introduction
00:00 • 3min
Exploring Deep Learning and Interdisciplinary Interests
03:20 • 21min
Challenges of Ensuring Consistent Performance and Accuracy in Production Machine Learning Models
24:06 • 13min
Preserving Information and Summarizing Partitions for Anomaly Detection
37:19 • 2min
Quantitative Statistics for Data Cleaning
39:15 • 18min
Challenges and Solutions in Machine Learning Workflows
56:53 • 3min
The Evolution from MLOps to LLMOps and FMOps
01:00:16 • 3min
The Role of Experimentation and Prompting in LLML
01:02:51 • 2min
Implementing Machine Learning Models and ML Monitoring
01:04:22 • 9min
Varying Perspectives on AI as a Product
01:13:50 • 3min