InfoQ Homepage QCon San Francisco 2024 Content on InfoQ
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QCon SF 2024 - Ten Reasons Your Multi-Agent Workflows Fail
At QCon SF 2024, Victor Dibia from Microsoft Research explored the complexities of multi-agent systems powered by generative AI. Highlighting common pitfalls like inadequate prompts and poor orchestration, he shared strategies for enhancing reliability and scalability. Dibia emphasized the need for meticulous design and oversight to unlock the full potential of these innovative systems.
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Stream All the Things: Patterns of Effective Data Stream Processing Explored by Adi Polak at QCon SF
Adi Polak, Director of Advocacy and Developer Experience Engineering at Confluent, illuminated the complexities of data streaming in her QCon San Francisco presentation. She outlined key design patterns for robust pipelines, emphasizing reliability, scalability, and data integrity.
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QCon San Francisco 2024 Day 3: Arch Evolution, Next Gen UIs, Staff+ and Hardware Architectures
The 18th annual QCon San Francisco conference was held at the Hyatt Regency San Francisco in San Francisco, California. This five-day event, organized by C4Media, consists of three days of presentations and two days of workshops. Day Three, scheduled on November 20th, 2024, included two keynote addresses by Hien Luu and Shruti Bhat and presentations from four conference tracks.
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Anna Berenberg Talks at QCon San Francisco on Google's One Network
Anna Berenberg, an Engineering Fellow at Google Cloud, unveiled One Network, a cloud-agnostic architecture that simplifies complex interconnected systems. Unifying disparate environments and leveraging open-source technologies enhances operational efficiency and consistency in security policies, empowering developers to focus on service endpoints while ensuring seamless platform integration.
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QCon SF 2024 - Scaling Large Language Model Serving Infrastructure at Meta
At QCon SF 2024, Ye (Charlotte) Qi of Meta tackled the complexities of scaling large language model (LLM) infrastructure, highlighting the "AI Gold Rush" challenge. She emphasized efficient hardware integration, latency optimization, and production readiness, alongside Meta's innovative approaches like hierarchical caching and automation to enhance AI performance and reliability.
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QCon SF 2024 - Incremental Data Processing at Netflix
Jun He gave a talk at QCon SF 2024 titled Efficient Incremental Processing with Netflix Maestro and Apache Iceberg. He showed how Netflix used the system to reduce processing time and cost while improving data freshness.
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Scaling OpenSearch Clusters for Cost Efficiency Talk by Amitai Stern at QCon San Francisco
Amitai Stern, engineering manager at Logz.io and OpenSearch Leadership Committee member delivered practical insights on efficient OpenSearch cluster management at QCon San Francisco. His session highlighted strategies for scaling effectively amidst fluctuating workloads, focusing on optimal shard management and resource allocation to minimize costs without compromising performance.
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QCon San Francisco 2024 Day 2: Shift-Left, GenAI, Engineering Productivity, Languages/Paradigms
The 18th annual QCon San Francisco conference was held at the Hyatt Regency San Francisco in San Francisco, California. This five-day event, organized by C4Media, consists of three days of presentations and two days of workshops. Day Two, scheduled on November 19th, 2024, included a keynote address by Lizzie Matusov and presentations from four conference tracks.
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QCon San Francisco 2024 Day 1: Architectures, Rust, AI/ML for Engineers, Sociotech Resilience
The 18th annual QCon San Francisco conference was held at the Hyatt Regency San Francisco in San Francisco, California. This five-day event, organized by C4Media, consists of three days of presentations and two days of workshops. Day One, scheduled on November 18th, 2024, included a keynote address by Khawaja Shams and presentations from four conference tracks.
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QCon SF 2024 - Why ML Projects Fail to Reach Production
Wenjie Zi of Grammarly addressed the high failure rates in machine learning at QCon SF 2024, revealing challenges from misaligned business goals to poor data quality. She advocated for a "fail fast" approach and robust MLOps infrastructure, emphasizing that learning from failures can drive success. Clear objectives and rigorous practices are essential for effective implementation.
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QCon SF 2024: Scale Batch GPU Inference with Ray
At QConSF 2024, Cody Yu presented how Anyscale’s Ray can more effectively handle scaling out batch inference. Some of the problems Ray can assist with include scaling large datasets (hundreds of GBs or more), ensuring reliability with spot and on-demand instances, managing multi-stage heterogeneous compute, and managing tradeoffs with cost and latency.
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Techniques and Trends in AI-Powered Search by Faye Zhang at QCon SF
At QCon SF 2024, Faye Zhang gave a talk titled Search: from Linear to Multiverse, covering three trends and techniques in AI-powered search: multi-modal interaction, personalization, and simulation with AI agents.
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QCon SF: Mandy Gu on Using Generative AI for Productivity at Wealthsimple
Mandy Gu spoke at QCon SF 2024 about how Wealthsimple, a Canadian fintech company, uses Generative AI to improve productivity. Her talk focused on the development and evolution of their GenAI tool suite and how Wealthsimple crossed the "Trough of Disillusionment" to achieve productivity.
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High-Performance Serverless with Rust: Insights from Benjamen Pyle’s Talk at QCon San Francisco
Benjamen Pyle's talk showcased the power of combining Rust with AWS Lambda for high-performance, scalable, serverless applications. He highlighted Rust's safety and efficiency and its ability to minimize cold start times and costs. Pyle emphasized best practices like multi-Lambda designs and infrastructure like code, enabling developers to build solutions that drive efficiency and sustainability.
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QCon SF: Large Scale Search and Ranking Systems at Netflix
Moumita Bhattacharya spoke at QCon SF 2024 about state-of-the-art search and ranking systems. She gave an overview of the typical structure of these systems and followed with a deep dive into how Netflix created a single combined model to handle both tasks.