Istio to The Rescue: How to Tame Your Microservices Architecture

Nowadays, everybody is running microservices. And why not? They have a lot of advantages, but to be honest, there are new drawbacks to stumble upon as well.

In this workshop, we will go through some of them and explain how they can be addressed thanks to a service mesh. First, there will be a brief explanation of what Istio architecture actually is and how it works. Afterwards, it is time to apply these concepts to a real demo cluster. Bit by bit, we will go through some scenarios and try to leverage them in Istio to help us solve them.

  • from 08.00 welcome desk open – coffee and snacks

  • 09.00 a.m. - 10.45 a.m.: Introduction, small presentation what is Istio and how it works (theory)

  • 10.45 a.m. - 11.15 a.m.: coffeebreak

  • 11.15 a.m. - 12.30 p.m.: Solve problems with installation and deployment. Security (Authn and Authz)

  • 12.30 p.m. - 1.30 p.m.: lunchbreak

  • 1.30 p.m. - 2.45 p.m.: Traffic management (Ingress/Egress traffic and traffic routing)

  • 2.45 p.m. - 3.00 p.m.: coffeebreak

  • 3.00 p.m. - 4.00 p.m.: Telemetry (tracing and metrics)

  • appr. 4.00 p.m. : end

Technical requirements:

  • I have written down the requirements here github document. It links to some other resources that will help the attendees to configure their systems.

If you are using hardware that is company property, please make sure, if one of the following problems could occur:

  • Workshop-participant has no administrator rights.

  • Corporate laptop uses excessively meticulous security software.

  • Set corporate-proxies, on which you are forced to communicate within the company, but which can’t connect in different environments.


* Basic knowledge of Kubernetes and/or Docker
* Experience using a terminal


* Present different approaches how to improve microservices architecture operations
* How a service mesh works



ab 8.30 Uhr Registrierung und Begrüßungskaffee

9.30 Uhr Beginn


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




Fernando Ripoll Fernando Ripoll is a software engineer who likes making things right. He has worked with different technology stacks like PHP, Node, golang, Ruby, Kubernetes, MongoDB and HTML5. As a frontend developer, he has created some rich user interfaces thanks to JavaScript frameworks such as Angular or Backbone. On the other hand, he has built REST APIs and middleware components as a backend developer, although nowadays he is mainly involved in trying to apply DDD concepts on a microservices platform.





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