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Open Source Summit Europe 2022: Open AI + Data Forum

machine learning engineers, data engineers, platform engineers, cloud engineers, open source maintainers, site reliability engineers, data scientists

neural search with DocArray; cloud-native Spark automation; Argo Workflows for Spark; confidential computing for ML; ONNX runtime integration; Kubeflow Pipelines v2 IR; open metadata and governance; open lineage with OpenLineage; deferrable operators in Airflow; vector databases (Milvus); dataset license compliance; explainability and adversarial robustness

titleauthorlength
Accessible Neural Search and Cloud-Nativeness for ML EngineersHan Xiao, Joan Fontanals Martínez36m
Open-source Jina provides DocArray for unified multimodal data, a minimal Python refactor into executor + flow units that spawns containerized microservices…
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Automating Cloud-native Spark Jobs with Argo WorkflowsCaelan Urquhart, Darko Janjić36m
Shows Argo Workflows + Kubernetes running containerized Apache Spark jobs so a single cluster hosts multiple Spark versions and avoids global installs. Demo…
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BlindAI, an Open-source Solution for Privacy-friendly AI DeploymentDaniel Huynh31m
BlindAI is an open source stack that uses Confidential Computing to keep data encrypted until it runs inside a hardware enclave, supports deployment of ONNX…
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Bring the Power of ONNX to Spark as it Never Happened BeforeWang Xiyuan, Yikun Jiang28m
Adds an ONNX Runtime execution provider for Huawei Ascend so ONNX models run directly on Ascend hardware via CANN/ATC/TBE instead of model translation, with CI…
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Bringing ML Workflows to Heterogeneous Cloud Native Machine Learning Pla...Tommy Li, Animesh Singh31m
KFP v2 IR provides a platform-neutral pipeline spec that lets Kubeflow Pipelines and Google Vertex AI share the same component-based representation; an…
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Community Data: What to Measure and Why?Cali Dolfi34m
Explains how community metrics move from raw counts to actionable insight by codifying questions into metrics and visualizations, using Augur OSS feeding a…
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Airflow: Save Tons of Money by Using Deferrable OperatorsKaxil Naik33m
Apache Airflow 2.2 adds deferrable (async) operators that suspend tasks on the worker, move polling to a central triggerer, and free worker slots; Astronomer…
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Building an Open Metadata and Governance EcosystemNigel Jones, David Radley39m
Egeria claims an open, federated metadata layer with a concrete set of open types and APIs that map technical columns to business glossary meanings, policies…
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How to Build Trustworthy AI with Open SourceDiana Dimitrova Atanasova, Teodora Sechkova32m
Practical recipe for trustworthy AI using open source tools: a convolutional neural network on MNIST is shown with high accuracy but easily flipped from class…
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OpenDataology - An Open Source Dataset License Compliance Project Initi...Gopi Krishnan Rajbahadur34m
An open source project that maps dataset licenses into an intermediate template and runs manual plus tooling-assisted compliance checks, showing that after…
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Truly Open LineageMandy Chessell32m
Three LF AI & Data projects integrate to record both deployed structure and actual runs: Egeria captures design metadata, OpenLineage defines an HTTP “Run”…
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Fyne UI Toolkit: Enabling a Truly Platform-Agnostic Development of Graphical Appl...Andrew B. Williams43m
Fyne is a Go GUI toolkit that builds native apps as single binaries for Linux (X11/Wayland), macOS, Windows, BSD and iOS/Android with no extra code changes…
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Extending SBOMs to Describe AI SystemsKaren Bennet, Kate Stewart43m
Proposal extends SPDX SBOMs to describe models and datasets by adding fields for training data provenance, consent, data drift detection, hyperparameters…
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The Evolution of Milvus: A Cloud-Native Vector DatabaseFrank Liu29m
Milvus 2.0 claims a production cloud-native vector database: a fully distributed, horizontally scalable system (v2.2) that disaggregates query, index, and data…
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Trusted and Responsible AI (Explainability, Adversarial, Bias, Fairness)Vamsi Mohan Vandrangi37m
Defines six concrete responsible‑AI principles — inclusiveness, accountability, reliability and safety, transparency, privacy and security — and recommends…
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Training AI To Code Using The Largest Code Dataset (Project CodeNet)Tommy Li, Animesh Singh42m
Project CodeNet is a 14 million–sample, 500 million–line corpus across 55 languages that the team uses with Project CodeNet subsets, DataShim and the Machine…
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