AI in Action: From Machine Learning Interpret...

Joyjit Roy shares his experience leading large-scale initiatives across insurance and eCommerce, including multi-year modernization programs that integrate AI into core enterprise systems. We explore what it takes to move from legacy platforms to intelligent, scalable systems tha ...  Show more

Key Highlights: Production-Scale LLMs: Deploying and scaling recommender systems powered by large language models. AI Infrastructure Challenges: Building reliable, high-performance search and ML platforms at global scale. MLOps & Model Optimization: Lessons learned from optimizin ...  Show more

The role of the data scientist is changing. Some organizations are splitting the role into more narrowly focused jobs, while others are broadening it. The latter approach, known as the Full Stack Data Scientist, is derived from the concept of a full stack software engineer, with ...  Show more

David Kanter, Executive Director at MLCommons, discusses the work they're doing with MLPerf Benchmarks, creating the world's first industry standard approach to measuring AI speed and safety. He also shares ways they're testing AI and LLMs for harm, to measure—and, o ...

What if AI could revolutionize healthcare with advanced language learning models? Sarah and Elad welcome Karan Singhal, Staff Software Engineer at Google Research, who specializes in medical AI and the development of MedPaLM2. On this episode, Karan emphasizes the importance of s ...  Show more

With AI tools constantly evolving, the potential for innovation seems limitless. But with great potential comes significant costs, and the question of efficiency and scalability becomes crucial. How can you ensure that your AI models are not only pushing boundaries but also deliv ...  Show more