Machine Learning (ML) and AI play a key role in modern software, and powers much of what we see and interact with. Each talk in this track covers a foundational area of ML, along with real-life use cases. Attendees will learn how ML works behind-the-scenes with software systems, as well as about tools, platforms, and algorithms that make up the discipline.
From this track
Verifiable and Navigable LLMs with Knowledge Graphs
Monday Nov 18 / 10:35AM PST
Graphs, especially knowledge graphs, are powerful tools for structuring data into interconnected networks. The structured format of knowledge graphs enhances the performance of LLM-based systems by improving information retrieval and ensuring the use of reliable sources.
Leann Chen
AI Developer Advocate @Diffbot, Creator of AI and Knowledge Graph Content on YouTube, Passionate About Knowledge Graphs & Generative AI
No More Spray and Pray— Let's Talk About LLM Evaluations
Monday Nov 18 / 11:45AM PST
The pace of development in AI in the past year or so has been dizzying, to say the least, with new models and techniques emerging weekly. Yet, amidst the hype, a sobering reality emerges: much of these advancements lack robust empirical evidence.
Apoorva Joshi
Senior AI Developer Advocate @MongoDB, 6 Years of Experience as a Data Scientist in Cybersecurity, Active Member of Girls Who Code, Women in Cybersecurity (WiCyS) and AnitaB.org
Recommender and Search Ranking Systems in Large Scale Real World Applications
Monday Nov 18 / 01:35PM PST
Recommendation and search systems are two of the key applications of machine learning models in industry. Current state of the art approaches have evolved from tree based ensembles models to large deep learning models within the last few years.
Moumita Bhattacharya
Senior Research Scientist @Netflix, Previously @Etsy, Specialized in Machine Learning, Deep Learning, Big Data, Scala, Tensorflow, and Python
Why Most Machine Learning Projects Fail to Reach Production and How to Beat the Odds
Monday Nov 18 / 02:45PM PST
Despite the hype around AI, many ML projects fail, with only 15% of businesses' ML projects succeeding, according to McKinsey. Particularly with the significant investments in large language models and generative AI, only a small portion of companies have managed to realize their true value.
Wenjie Zi
Senior Machine Learning Engineer and Tech Lead @Grammarly, Specializing in Natural Language Processing, 10+ Years of Industrial Experience in Artificial Intelligence Applications
Unconference: AI and ML for Software Engineers
Monday Nov 18 / 03:55PM PST
Reinforcement Learning for User Retention in Large-Scale Recommendation Systems
Monday Nov 18 / 05:05PM PST
This talk explores the application of reinforcement learning (RL) in large-scale recommendation systems to optimize user retention at scale - the true north star of effective recommendation engines.
Saurabh Gupta
Senior Engineering Leader @Meta, Veteran in the Video Recommendations Domain, Helping Scale Video Consumption
Gaurav Chakravorty
Uber TL @Meta, Previously Worked on Facebook Video Recommendations and Instagram Friending and Growth
