Building ML workflows with Java, Python & Apacha Beam by Robbe Sneyders

streaming data processing pipelines that provides a variety of language SDKs such as Python, Java, Go, and more. This enables the creation of multi-language pipelines where each step can be implemented in the most suitable language. This enables collaboration between teams with different language preferences, and across domains with different default languages, such as Java for data processing and Python for ML modeling. In this talk we'll look at the Apache Beam framework and how to use it to orchestrate batch and streaming ML workflows with Python and Java components. ROBBE SNEYDERS Robbe is the Head of Delivery at ML6, a machine learning services company founded in Belgium with international offices throughout Europe. He leads the technical team and is responsible for the successful and high quality delivery of their projects. Robbe considers himself a machine learning generalist, but has especially deep knowledge about machine learning systems design with a focus on representation learning based systems and the MLOps methodology. Robbe is a big fan of open source. He's a maintainer and leading developer open source Connexion API framework and contributes to the wider Python API ecosystem. He made some major contributions to Apache Beam, on which he delivered a keynote at the European Beam summit. He's also been featured as a speaker on Tensorflow meetups, Google Cloud meetups, and ML conferences across Europe.