Jörg Schad
where else to find them
what they talk about
all 15 in the filters
where they keep going back
- KubeCon + CloudNativeCon Europe3 editions2018–2022
- DevOpsCon2 editions2017–2019
all 10 in the filters
on a stage with
- Max Neunhöffer2×
- Adam Janikowskionce
- Chris Gaunonce
all 8 in the filters
who talks about the same things
- Johannes Unterstein7 talks
- David Greenberg8 talks
- Deepak Goel4 talks
- Ben Hindman17 talks
- Dario Tranchitella12 talks
- Michele Bustamante10 talks
| title | conference | date |
|---|---|---|
| Graph ML – The Next Level Of Machine Learning; Learn How To Use It And When Not To | Big Data LDN 2022 | 2022-09-21 |
Claims that graphs boost fraud detection, ranking (PageRank), recommendations and chemistry by treating relationships as first‑class data and noting about 30%… | ||
| Transparent Live Migration of Services Between Kubernetes Cluster | KubeCon + CloudNativeCon Europe 2022 | 2022-05-16 |
ArangoDB proves live, cross-region and cross-cloud migration of stateful clusters with its Kube-Arango operator, migrating individual DB members one-by-one… | ||
| gVisor, Kata Container, Firecracker, Docker: Who is Who in the Container Space | DevOpsCon 2019 | 2019-12-02 |
Containers* have revolutionized the IT landscape and for a long time Docker seemed to be the the default whenever people were talking about containerization… | ||
| A Tale of Two Worlds: Canary-Testing for Both ML Models and Microservices | KubeCon + CloudNativeCon Europe 2019 | 2019-05-20 |
Demo shows Kubernetes + Vamp (fem/ctl) driving canary releases for both a microservice and its ML model, wiring metrics from Prometheus/Elasticsearch and a UI… | ||
| Operating Deep Learning Pipelines Anywhere Using Kubeflow | KubeCon + CloudNativeCon China 2018 | 2018-11-13 |
Shows how to assemble a production-grade ML pipeline on Kubernetes using Kubeflow, with notebook-driven model engineering, distributed training and autoscaling… | ||
| Deep learning beyond the learning | Codemotion Amsterdam 2018 | 2018-05-08 |
Pipeline covers training to production with concrete choices: use Kubeflow or cloud ML engines for notebooks and orchestration, store hundreds of model… | ||
| Bringing Your Data Pipeline into The Machine Learning Era | KubeCon + CloudNativeCon Europe 2018 | 2018-05-02 |
Shows a concrete pipeline that reads distributed data running on Apache Mesos and feeds it into TensorFlow running on Kubernetes via Kubeflow on Kubernetes… | ||
| Deep learning beyond the learning | Codemotion Rome 2018 | 2018-04-13 |
Covers the full deep learning pipeline with concrete tooling and numbers: training hundreds to thousands of models via hyperparameter sweeps, storing model… | ||
| NO ONE PUTS Java IN THE CONTAINER | Codemotion Milan 2017 | 2017-11-10 |
Containers use cgroups and namespaces so processes see the host kernel but not full isolation, and the JVM often misreads host resources: pre‑Java 9 reads… | ||
| Welcome Remarks | MesosCon Europe 2017 | 2017-10-25 |
A quick opening polls the room about starting earlier versus sleeping longer and records about half the audience favoring a later start, so the schedule stays… | ||
| Container Networking for Micro-Services: An Apache Mesos Networking Deep Dive | MesosCon Europe 2017 | 2017-10-25 |
Covers how DC/OS and Mesos wire containers end to end: the CNI isolator creates network namespaces and plugins (bridge, IPAM, VLAN) attach them; overlay… | ||
| Apache Flink Meets Apache Mesos and DC/OS | MesosCon Europe 2017 | 2017-10-25 |
Explains a concrete Mesos/DC/OS integration: Flink becomes a Mesos framework using Fenzo offer matching and a new FLIP-6 architecture that adds a long‑running… | ||
| Disaster Recovery with a Distributed Database | MesosCon Europe 2017 | 2017-10-25 |
ArangoDB implements asynchronous DC-to-DC replication by dumping change events into Kafka on each side and replaying them with ArangoSync so a primary cluster… | ||
| Keynote: Welcome to Day 2 | MesosCon North America 2017 | 2017-09-13 |
Morning recap emphasizes practical outcomes: a hackathon delivers UI and docs improvements and demo code destined for Mesosphere, a Kubernetes milestone gets… | ||
| Container Networking for Micro-Services: An Apache Mesos and DC/OS Networking Deep Dive | MesosCon North America 2017 | 2017-09-13 |
Explains how DC/OS uses Mesos + CNI to give IP-per-container over VXLAN overlay, with the master slicing a global subnet into per-agent prefixes (example… | ||
| Distributed Data on Distributed Infrastructure | MesosCon North America 2017 | 2017-09-13 |
ArangoDB runs as a native multi‑model database on Mesos with a single query language, per‑collection replication factor 2 and shard placement; early… | ||
| Doing “DropBox” the Cloud Native Way | MesosCon North America 2017 | 2017-09-13 |
Rebuilds a Dropbox-like service on DC/OS using containerized pieces: web frontend in Go, API servers, Minio object storage, Redis for metadata and MySQL for… | ||
| Nightmares of a Container Orchestration System | DevOpsCon 2017 Berlin | 2017-06-12 |
Practical failures and fixes for Mesos/DC/OS clusters: require immutable, versioned images and a private registry to avoid silent changes; monitor disk and… | ||
| HTTP API In Action | MesosCon Asia 2016 | 2016-11-18 |
Mesos exposes three HTTP surfaces — scheduler, executor, operator APIs — that replace libmesos RPCs, enable language‑independent frameworks, and use POST… | ||
| Deploying Fast Data Pipelines | MesosCon Asia 2016 | 2016-11-18 |
Overview of deploying Fast Data on Mesos/DC/OS with concrete choices and tradeoffs: uses Kafka + Spark Streaming + Elasticsearch and optionally Flink… | ||
| Writing Stateful Frameworks | MesosCon Asia 2016 | 2016-11-18 |
Mesos now provides persistent volumes and dynamic reservations but writing failover-safe stateful schedulers remains hard — an initial Cassandra framework grew… | ||
| nothing matches | ||