At QCon San Francisco 2024, the international software development conference by InfoQ, there are two tracks dedicated to the rapid advancements in AI and ML, reflecting how these technologies have become central to modern software development.
The conference spotlights senior software developers from enterprise organizations, sharing their approaches to adopting emerging trends. Each session provides actionable insights and strategies attendees can immediately apply to their own projects. Each talk is led by seasoned practitioners who share actionable knowledge—no marketing pitches, just real-world solutions.
The first track, Generative AI in Production and Advancements, curated by Hien Luu, Sr. engineering manager @Zoox & author of MLOps with Ray, provides a deep dive into practical AI/ML applications and the latest industry innovations. The track explores real-world implementations of GenAI, focusing on how companies leverage it to enhance products and customer experiences.
Talks will include insights from companies like Pinterest, Meta, Microsoft, Voiceflow, and more, sharing best practices on deploying large language models (LLMs) for search and recommendations and exploring AI agents' potential for the future of software:
- Scaling Large Language Model Serving Infrastructure at Meta: Charlotte Qi, senior staff engineer @Meta, will share insights on balancing model quality, latency, reliability, and cost, supported by real-world case studies from their production environments.
- GenAI for Productivity: Mandy Gu, senior software development manager @Wealthsimple, will share how they use Generative AI to boost operational efficiency and streamline daily tasks, blending in-house tools with third-party solutions.
- Navigating LLM Deployment: Tips, Tricks, and Techniques: Meryem Arik, co-founder @TitanML, recognized as a technology leader in Forbes 30 Under 30, will cover best practices for optimizing, deploying, and monitoring LLMs, with practical tips and real case studies on overcoming the challenges of self-hosting versus using API-based models.
- LLM Powered Search Recommendations and Growth Strategy: Faye Zhang, staff software engineer @Pinterest, covers model architecture, data collection strategies, and techniques for ensuring relevance and accuracy in recommendations, with a case study from Pinterest showcasing how LLMs can drive user activation, conversion, and retention across the marketing funnel.
- 10 Reasons Your Multi-Agent Workflows Fail and What You Can Do about It: Victor Dibia, principal research software engineer @Microsoft Research, core contributor to AutoGen, author of "Multi-Agent Systems with AutoGen" book, will share key challenges in transitioning from experimentation to production-ready systems and highlight 10 common reasons why multi-agent systems often fail.
- A Framework for Building Micro Metrics for LLM System Evaluation: Denys Linkov, head of ML @Voiceflow, LinkedIn Learning Instructor, ML advisor, and instructor, explores how to measure and improve LLM accuracy using multidimensional metrics beyond a simple accuracy score.
The second track, "AI and ML for Software Engineers: Foundational Insights", curated by Susan Shu Chang, principal data scientist @Elastic, author of "Machine Learning Interviews", is tailored to those looking to integrate AI/ML into their work.
The talks will cover the deployment and evaluation of ML models, providing hands-on lessons from companies that have faced similar challenges:
- Recommender and Search Ranking Systems in Large Scale Real World Applications: Moumita Bhattacharya, senior research scientist @Netflix, dives into the evolution of search and recommendation systems, from traditional models to advanced deep learning techniques.
- 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, will explore why many machine learning projects fail, despite the growing hype around AI, and how to avoid common pitfalls such as misaligned objectives, skill gaps, and the inherent uncertainty of ML.
- Verifiable and Navigable LLMs with Knowledge Graphs: Leann Chen, AI developer advocate @Diffbot, creator of AI and knowledge graph content on YouTube, will demonstrate how knowledge graphs enhance factual accuracy in responses and how their relationship-driven features enable LLM-based systems to generate more contextually-aware outputs.
- Reinforcement Learning for User Retention in Large-Scale Recommendation Systems: Saurabh Gupta, senior engineering leader @Meta, and Gaurav Chakravorty, Uber TL @Meta, will share insights into leveraging reinforcement learning (RL) for personalized content delivery, exploring reward shaping, optimal time horizons, and necessary infrastructure investments for success at scale.
- No More Spray and Pray— Let's Talk about LLM Evaluations: Apoorva Joshi, senior AI developer @MongoDB, six years of experience as a data scientist in cybersecurity, active member of Girls Who Code, Women in Cybersecurity (WiCyS) and AnitaB.org, will share practical frameworks for LLM evaluation that someone can apply directly to their projects.
These sessions aim to bridge the gap between AI hype and practical, scalable applications, helping engineers of all levels navigate the evolving AI landscape.
Explore all 12 tracks taking place at QCon San Francisco this November 18-22, and take advantage of the last early bird tickets ending on October 29. With only a few weeks left, join over 1,000 of your peers in shaping the future of software development!
