Tech Talks

Did you miss our Tech Talk series this September? No problem – we recorded them for you! Our online sessions are targeting Data Scientists, Data Engineers, Designer, and Product Owners. We share with you insights into highly relevant topics in the area of Big Data, Machine Learning, and Industrial applications. All Tech Talks have a theoretical part as well as use cases from the real-world.

CI/CD for Machine Learning

In software development, Continuous integration and delivery (CI/CD) is widespread and used to iterate faster. Machine Learning Models depend however not only on the code but also on data and other parameters. Thus, the topic of CI/CD is more complex for ML models. In this talk, you will get an introduction into CI/CD for machine learning, open-source tools, and one on-premise use case with Jenkins. 

What you will learn

  • What are the challenges of CI/CD for ML especially

  • What tools can be used to handle data and retraining with CI/CD

  • Concurrent usage of on-premise hardware

  • One practical example of a real-world application

Target Audience

Data Engineers, Data Ops, Data Scientists


DI Bernhard Redl

Data Engineer

DI Lukas Haselsteiner

Software Engineer

UI/UX for industrial settings

While great attention is paid to the user interface in the B2C area, this is often neglected in industrial applications. However, usability is a success factor for industrial solutions as well: The work with well-designed interfaces is more profitable due to faster error prevention and fewer operating errors. 

In this session, we discuss a human-centered design process for building industrial software solutions that in the end can be used smoothly, effectively, and efficiently. Further, we will demonstrate what usability can look like in real-world use cases.

What you will learn

  • Basic concepts and values of designing in a user-centered approach. 

  • How a user-centered design approach can be followed when building industrial solutions. 

  • Examples of real world use cases.

Target Audience

Product Owner, Designer, Software Engineer


Elisabeth Ettinger, MSc

UX Designer

Michael Hettegger, BSc


Intro to Spark and Databricks

This Tech Talk will give you an introduction to Spark and how to write and execute Spark code with Databricks and what it is needed for. Apache Spark is an open-source distributed general-purpose cluster-computing framework. Currently its the leading platform for large-scale SQL, batch processing and stream processing. Azure Databricks is an Apache Spark-based analytics platform and accelerates innovation by unifying data science, engineering and business. Join this session if you are interested to get to know Spark and Databricks better and how to use it.

What you will learn

  • What is Spark and when do I need to use it?

  • Databricks Basics and how to setup Azure Databricks

  • How to write basic PySpark code

  • PySpark & Databricks in action: demonstrated with a use case

Target Audience

Data Engineers, Data Ops, Data Scientists


Markus Muth, BSc

Data Engineer

Simon Gavris

Data Engineer


In this session, we will focus on Spark and how to use the framework for Machine Learning. We will walk you through the basic concepts and show you how Spark can efficiently perform data exploration, cleaning, aggregations, and train ML models. In the tech talk, we will also guide you through a use case backing up the theory. 

What you will learn

  • Basic concepts

  • Transformers, Estimators, Pipelines

  • Data cleaning and Feature engineering

  • Pipeline training, selection and evaluation

Target Audience

Data Scientists, Data Engineers


Markus Muth, BSc

Data Engineer

Yuri Lifanov, Ph.D

Data Scientist

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