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Once AI is part of a critical system, model quality is only one control. Evals, inference, reliability, and autonomous workflows matter too.
The Engineering AI Systems track at QCon San Francisco connects those decisions through six production case studies.
Review the track: buff.ly/hz1btG2
Six sessions connect applied AI architecture at BlackRock, retrieval and ranking at LinkedIn, ML serving at Netflix, evals, self-improving agents, and agentic software development.
Giving every engineer a coding agent does not create shared team capability.
Read Brian Yang’s QCon AI New York session abstract: buff.ly/pT1T519
At Spotify, running a coding agent across an entire fleet means dealing with Maven, Yarn, Bazel, and custom build scripts, all with different commands.
Jo Kelly-Fenton, Engineer at Spotify, explains how Spotify built a single verify tool that routes each repository to the right build and test system, giving the agent one interface for verification regardless of the underlying toolchain.
More sessions like this are lined up for QCon London 2027, April 13-16. Find out more about QCon London 2027 here: buff.ly/0Ut0uuP
#QConLondon #CodingAgents #DeveloperExperience
Chunking for RAG has two separate context-window constraints — and the embedding model can impose the tighter one.
Lan Chu, AI Tech Lead and Senior Data Scientist at Rabobank, explains: the largest embedding models top out at around 8,000 tokens, so chunking decisions need to account for the model creating the embeddings, not only the model generating the answer.
More sessions like this are lined up for QCon London 2027, April 13-16.
Find out more about QCon London 2027 here: buff.ly/6cJwvMB
#QConLondon #RAG #EmbeddingModels
The longer a stateful Flink job runs, the more state it accumulates — eventually managing that state takes more CPU than the computation itself.
Giannis Polyzos, Principal Streaming Architect at Ververica, explains why compute-storage separation is a central design goal in Apache Fluss: separating growing state from the stream processor changes where the maintenance burden sits.
More sessions like this are lined up for QCon London 2027, April 13-16.
Find out more about QCon London 2027 here: buff.ly/ohGB1Dr
#QConLondon #ApacheFluss #StreamProcessing
Recommendations at Airbnb were four orders of magnitude larger than fraud detection: each search ranked thousands of homes, each with thousands of features — roughly 1 trillion operations per request.
Nikhil Simha, Co-Founder & CTO at zipline.ai, explains how the Chronon engine was rewritten to pre-compute most of that work, while keeping the API identical so nothing upstream broke.
More sessions like this are lined up for QCon London 2027, April 13-16.
Find out more about QCon London 2027 here: buff.ly/hmdyzHB
#QConLondon #FeatureStore #MLInfrastructure
BlackRock’s Asimov separates data, intelligence/context, and applications/agents.
Read the session abstract: buff.ly/dpVvgaF
Randy Shoup’s keynote at QCon London 2027 starts with a 9x gap between the highest-performing and median engineering organizations.
Read the full abstract: buff.ly/cF4TStp
