InfoQ Homepage QCon San Francisco 2024 Content on InfoQ
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Learnings from Internal Tool Migrations to Support Software Engineering Efficiency
In her presentation at QCon San Francisco, Ying Dai shared two critical software engineering migration stories - one focused on production monitoring and the other on production deployments with automated validations. Both migrations were driven by the goal of enhancing engineering efficiency, but each came with its own challenges and lessons.
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Exploring Factors that Drive Software Engineering Productivity
Understanding what drives software development productivity is the key to making high-impact investments in engineering productivity, Emerson Murphy-Hill said at QCon San Francisco. He presented the results of their investigation into factors that predict developer productivity and shared what they learned from exploring inequities in software engineering.
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QCon SF 2024: Refactoring Large, Stubborn Codebases
Jake Zimmerman, technical lead of Sorbet at Stripe, and Getty Ritter, Ruby infrastructure engineer at Stripe, presented Refactoring Stubborn, Legacy Codebases at the 2024 QCon San Francisco conference. Zimmerman and Ritter demonstrated how to fix complaints on maintaining a large codebase with leverage and by ratcheting incremental progress.
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Micro Metrics for LLM System Evaluation at QCon SF 2024
Denys Linkov's QCon San Francisco 2024 talk dissected the complexities of evaluating large language models (LLMs). He advocated for nuanced micro-metrics, robust observability, and alignment with business objectives to enhance model performance. Linkov’s insights highlight the need for multidimensional evaluation and actionable metrics that drive meaningful decisions.
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How Slack Used an AI-Powered Hybrid Approach to Migrate from Enzyme to React Testing Library
Enzyme’s lack of support for React 18 made their existing unit tests unusable and jeopardized the foundational confidence they provided, Sergii Gorbachov said at QCon San Francisco. He showed how Slack migrated all Enzyme tests to React Testing Library (RTL) to ensure the continuity of their test coverage.
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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.