Speaker
Abstract
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. This brings several modeling, systems, infrastructural and software challenges and improvements with it. Additionally, Large Language Models and Foundation Models are also rapidly starting to influence the capabilities of these real world search and recommender systems.
In this talk, Moumita will present an overview of industry search and recommendations systems, go into modeling choices, data requirements and infrastructural requirements, while highlighting challenges typically faced for each and ways to overcome them.
Topics
QCon San Francisco 2024 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
AI and ML for Software Engineers: Foundational Insights Hosted by Susan Shu Chang Principal Data Scientist @Elastic, Author of "Machine Learning Interviews"From the same track
Monday 18 November
10:35 Ballroom BC Session Knowledge Graphs Enhance LLMs’ Explainability and Trustworthiness With Knowledge Graphs Leann Chen AI Developer Advocate @Diffbot, Creator of AI and Knowledge Graph Content on YouTube, Passionate About Knowledge Graphs & Generative AI 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. 11:45 Ballroom BC Session Scale Out Batch Inference with Ray Cody Yu Staff Software Engineer and Tech Lead @Anyscale, Ex-Amazonian, vLLM Committer, Apache TVM PMC As AI technologies continue to evolve, the demand for processing both structured and unstructured data across diverse industries is rapidly growing. 13:35 Ballroom BC Session AI/ML Recommender and Search Ranking Systems in Large Scale Real World Applications Moumita Bhattacharya Senior Research Scientist @Netflix, Previously @Etsy, Specialized in Machine Learning, Deep Learning, Big Data, Scala, Tensorflow, and Python 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. 14:45 Ballroom BC Session AI/ML Why Most Machine Learning Projects Fail to Reach Production and How to Beat the Odds Wenjie Zi Senior Machine Learning Engineer and Tech Lead @Grammarly, Specializing in Natural Language Processing, 10+ Years of Industrial Experience in Artificial Intelligence Applications 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. 15:55 Seacliff D Unconference Unconference: AI and ML for Software Engineers 17:05 Ballroom BC Session AI/ML Reinforcement Learning for User Retention in Large-Scale Recommendation Systems Saurabh Gupta, Gaurav Chakravorty 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.