Detecting Hidden Bias in Datasets Before Models Fail
Hidden bias in datasets silently breaks machine learning systems in production. This talk shows how to detect data imbalance, leakage, and coverage gaps early using practical metrics, visualizations, and open-source tools—before misleading offline metrics turn into costly real-world failures.
Floe: Policy-Based Table Maintenance for Apache Iceberg
Iceberg maintenance procedures work. Orchestrating them across hundreds of tables is the problem. Floe is an open-source system that treats maintenance as policy: glob patterns, schedules, and health-driven triggers that gate operations on real table metrics. Supports 7 catalogs, executes via Spark or Trino.
Escaping the Cloud: High-Performance AI in your Browser
Server-side inference is the bottleneck of modern AI, creating costs and privacy hurdles. But what if the solution is scaling down to the browser? This session investigates Client-Side AI using WebGPU, ONNX Runtime, and Transformers.js. We’ll explore the reality of hardware access, model size, and the 2026 trade-offs of browser based execution.
What you should know about constraints in PostgreSQL 18
This talk explains how constraints work in Postgres by exploring the pg_constraint catalog and core concepts like table vs. column constraints, constraint triggers, domains and constraint deferrability through SQL queries. It then covers what’s new in Postgres 18 including temporal keys, NOT NULL as a first-class constraint, NOT ENFORCED and more.
Beyond the Hype: When Apache Flink Solves Real Problems
When does Apache Flink solve real problems versus add complexity? Explore use cases where Flink becomes essential such as fraud detection, CDC, real-time analytics versus when batch or Kafka Streams suffice. Compare stream engines (Flink, Spark) with platforms (Kafka, Pulsar) to confidently decide when streaming delivers value.
Event-driven Agents with Complex Event Processing in Flink
Event-driven Agents calling LLMs can be combined with Pattern Recognition and Anomaly Detection in Apache Flink in smart ways to increase cost efficiency, avoid hallucinations and enforce predictable, deterministic behavior. Specifically in a business process context, this architecture provides opportunities for continuous real-time process mining.
Why Choose One: Multi-Engine Analytics with Apache Wayang
Choosing the best engine for each data task sounds right, but in modern data stacks doing so requires expertise and effort. Apache Wayang, a recently graduated TLP, addresses this by decoupling logical dataflows from execution engines. From big data platforms to SQL and ML engines, Wayang enables cross-platform execution that maximizes performance.
Apache Spark Declarative Pipelines in Action
Learn Spark 4.1’s brand-new Declarative Pipelines, a paradigm shift replacing imperative code with simple declarations. We’ll build a real-time data pipeline together, processing streaming ADS-B flight data from tens of thousands of aircraft overhead.
Mentoring In Open Source in the Age of AI
Open source mentorship changed overnight with AI tools. Contributors submitted polished code they couldn’t explain, making learning harder to assess. This talk shares what we learned mentoring Outreachy contributors—what failed, what worked, and what we’re still figuring out.
AI in the physical world: from observation to discovery
In 2026, AI is moving beyond digital tasks into the physical world. It increasingly interacts with instruments, experiments, and real-world data. Physicists stand at this frontier, using deep learning, LLMs, and agents to analyze nature itself. What have we learned about AI when it meets reality?