Kubernetes Advanced

Kubernetes provides a set of more then thirty resource types out of the box and that alone might look overwhelming for a beginner. Still these gain new features with each release and with the popularity of the operator pattern, more and more custom resources show up in our Kubernetes clusters.

In this workshop we'll focus on some more advanced topics, explain their background and how to use them to get a better overview and more confidence in the stability of a more complex Kubernetes setup.

Agenda
  • ab 10.00 Registrierung und Begrüßungskaffee

  • 11.00: Beginn

  • 12.30 - 13.30: Mittagspause

  • 15.00 - 15.15: Kaffeepause

  • 16.30 - 16.45: Kaffeepause

  • ca. 18 Uhr: Ende

Vorkenntnisse

* Microservice architecture
* Kubernetes basic resource types like Pod, ConfigMap, Secret, Deployment, Service, Ingress
* Running Minikube on a local laptop

Lernziele

* Custom resources and operators
* Application configuration management
* Planning workload distribution using resource quotas and Pod priority and preemtion
* Scaling workloads on demand
* Securing workloads via Role-based access control and Pod Security Policies
- Using vault for secret management

 

Agenda

ab 8.30 Uhr Registrierung und Begrüßungskaffee

9.30 Uhr Beginn

Intro

Machine Learning

  • Was ist Machine Learning?
  • Der typische ML Workflow
  • Was sind neuronale Netze?
  • Jupyter Lab mit Python
  • Eine Einführung in TensorFlow
  • Keras als High-Level API für TensorFlow

Praxisteil: Deep Learning Modelle mit Keras

  • Datengeneratoren
  • Datasets explorativ analysieren
  • Hold-Out vs. Cross Validation

11.00 - 11.15 Uhr: Kaffeepause

Praxisteil: Deep Learning Modelle mit Keras

  • Feed-Forward Netzarchitektur
  • Convolutional Neural Networks als Deep Learning Ansatz
  • Evaluation und Visualisierung des Modells

12.30 - 13.30 Uhr: Mittagspause

Pipelines mit Luigi

  • Anforderungen an produktive Modelle
  • Übersicht über Luigi und dessen Module
  • Bau eines Beispiel-Workflows

Praxisteil: Den Keras-Workflow mit Luigi implementieren

  • Anforderungen an produktive Modelle
  • Übersicht über Luigi und dessen Module
  • Bau eines Beispiel-Workflows

15.30 - 15.45 Uhr: Kaffeepause

Praxisteil: TensorFlow-Serving

  • Übersicht über TensorFlow-Serving
  • Ladestrategien konfigurieren
  • Deployment des Modells

ca. 17.00 Uhr: Ende

 

Referent

 

Tobias Bradtke Tobias Bradtke has experience with Linux and open-source software for more than twenty years and he started playing with Docker with the very first release. Obviously, Kubernetes is currently his favorite topic, earlier on that list were among others Python, Elasticsearch, NoSQL databases, Neo4j, Javascript, Ruby and different web frameworks. In his spare time he is voluntary committed to work on Open Data projects within the local community. At Giant Swarm he takes care of customer happiness as a Solution Engineer. Foremost this is about understanding customer needs, following new development across the Kubernetes ecosystem and also shaping the road map of the Giant Swarm platform.

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