- 11:45
Cinema 3

Automating the lifecycle of Machine Learning applications

Machine learning application development is becoming popular nowadays. The process of developing machine learning applications is often more complex compared to traditional software development. This is due to the nature of machine learning, where behavior is often hard to predict, hard to reproduce, thus hard to test and therefore difficult to bring into production. How does one setup and automate a pipeline and incorporate a workflow that is suitable for machine learning?

During this presentation we will walk through on how we setup a continuous integration & continuous deployment pipeline and how we test, train and deploy machine learning applications. We also show how we incorporate a workflow that let data scientists focus on machine learning rather than infrastructure.

About the speakers


Ward van Breda

Lead Data Scientist at Stedin

Ward is a passionate research scientist in the field of artificial intelligence, who now works as lead data scientist at Stedin. Ward has applied artificial intelligence in the domain of healthcare during his PhD, and has since worked in the domains of psychology, finance, legal counsel, and energy. Ward currently focusses on how to increase success and optimize impact of data science projects in business.


Steven Ramdas

Data engineer at Luminis /Amsterdam

Steven Ramdas is eager to apply his skillset to the field of data- & machine learning engineering. Steven has strong background in Software Engineering and developed a mindset from startup environments. Steven is on a mission to help data scientist’s deliver production ready machine learning systems.

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