InfoQ Homepage News
-
Defensible Moats: Unlocking Enterprise Value with Large Language Models at QCon San Francisco
In a recent presentation at QConSFrancisco, Nischal HP discussed the challenges enterprises face when building LLM-powered applications using APIs alone. These challenges include data fragmentation, the absence of a shared business vocabulary, privacy concerns regarding data, and diverse objectives among stakeholders.
-
The Challenges of Producing Quality Code When Using AI-Based Generalistic Models
Using AI with generalistic models to do very specific things like generating code can cause problems. Producing code with AI is like using code from someone else who you don’t know which may not match your standards and quality. Creating specialised or dedicated models can be a way out.
-
ReSharper 2023.2 IDE Includes Predictive Debugger
At the beginning of August, JetBrains introduced a predictive debugger in the 2023.2 version of ReSharper, its developer productivity extension for Microsoft Visual Studio. The predictive debugger anticipates the future of code execution without actually executing the code and provides visual cues to aid in understanding code behavior.
-
Lessons from Leading the Serverless First Journey at Capital One: George Mao at QCon San Francisco
During the third day of QCon San Francisco, George Mao, a senior distinguished engineer at Capital One, presented on his company's journey into serverless, the best practices they picked up, and the lessons learned along the way. The session was part of the “Architecting for the Cloud” track.
-
Practical Advice for Retrieval Augmented Generation (RAG), by Sam Partee at QCon San Francisco
At the recent QCon San Francisco conference, Sam Partee, principal engineer at Redis, gave a talk about Retrieval Augmented Generation (RAG). He discussed Generative Search, which combines large language models (LLMs) with vector databases to improve information retrieval. Partee discussed several innovative tricks such as Hypothetical Document Embeddings (HyDE), and semantic caching.
-
Chronon - Airbnb’s End-to-End Feature Platform at QCon SF 2023
At QConSF, Airbnb staff software engineer Nikhil Simha presented Chronon, Airbnb's solution to address the challenges of managing and serving the vast number of features used in machine learning models. The platform focuses on four key areas: core APIs, training data generation, feature serving, and feature observability.
-
Disaster Recovery Across a Million Pieces: Michelle Brush at QCon San Francisco
During the second day of QCon San Francisco 2023, Michelle Brush, an engineering director, SRE at Google, discussed challenges, patterns, and practices for disaster recovery actions in massively distributed systems in her session. The session is part of the "Designing for Resilience" track.
-
Effective Performance Engineering at Twitter-Scale: Yao Yue at QCon San Francisco
During the second day of QCon San Francisco 2023, Yao Yue, the founder of IOP Systems, presented on performance engineering. In her session Yue discussed the evolving performance engineering in the modern era. For decades, hardware advancements have kept many performance engineers on the sidelines, but now, in a pivotal moment, their skills are more crucial than ever.
-
Distributed Materialized Views: How Airbnb’s Riverbed Processes 2.4 Billion Daily Events
Airbnb created Riverbed, a Lambda-like data framework for producing and managing distributed materialized views. The framework supports over 50 read-heavy use cases where data is sourced from multiple data sources within the company’s service-oriented architecture (SOA) platform. It uses Apache Kafka and Apache Spark for online and offline components, respectively.
-
Generative AI: Shaping a New Future for Fraud Prevention, by Neha Narkhede at QCon San Francisco
At the recent QCon San Francisco conference, Neha Narkhede gave a keynote on how generative AI can help improve the state of the art in fraud prevention. She discussed the "knowledge fabric", which is able to capture all information and knowledge on current fraud methods. She also introduced six foundational pillars of AI Risk Decisioning.
-
QCon San Francisco 2023 Day 1: Architectures, Data Engineering, Infra Languages, Staff+ Skills
The 17th 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 October 2nd, 2023, included a keynote address by Suhail Patel and presentations from four conference tracks and two sponsored tracks.
-
Managing 238 Million Memberships of Netflix: Surabhi Diwan at QCon San Francisco
During the first day of QCon San-Francisco 2023, Surabhi Diwan, a senior software engineer at Netflix, presented on managing 238 million Memberships of Netflix. The talk is a part of the “Architectures You’ve Always Wondered About" track. Diwan's work at Netflix involves the backend work regarding membership engineering, which is critical for both signups and streaming at Netflix.
-
Canonical Launches Charmed MLFlow to Simplify Management and Maintenance of ML Workflows
Based on the open-source MLflow platform, Canonical Charmed MLFlow aims to simplify the task of managing machine learning workflows and artifacts by using alternative packaging system and orchestration engine.
-
GitHub's Learnings from Building Copilot, an Enterprise LLM Application
GitHub has published an article containing the lessons they learned in building and scaling GitHub Copilot -- an enterprise application using an LLM (Large Language Model). In a post on GitHub's blog, AI product leader Shuyin Zhao describes how -- over three years -- they broke the project down into three stages - "find it", "nail it" and "scale it", and successfully launched GitHub Copilot.
-
Unpacking How Ads Ranking Works @ Pinterest: Aayush Mudgal at QCon San Francisco
At QCon San Francisco, Aayush Mudgal gave a talk on Pinterest's ad ranking strategy. Pinterest does both candidate retrieval and ranking, supported by user interaction data and what they are currently watching. They use neural networks to create embeddings for ads and users, where ads which are close to the user should be relevant. They train and deploy models on a daily basis.