The only certainty in life is that the pendulum will always swing. Recently, the pendulum has been swinging towards repatriation. However, the infrastructure needed to build and operate AI systems using Python in a sovereign (even air-gapped) environment has changed since the shift towards the cloud. This talk will introduce the infrastructure you need to build and deploy Python applications for AI - from data processing, to model training and LLM fine-tuning at scale to inference at scale. We will focus on open-source infrastructure including: a Python library server (Pypi, Conda, etc) and avoiding supply chain attacks a container registry that works at scale a S3 storage layer a database server with a vector index

You've probably heard the name Apache Iceberg by now. If it wasn't when Databricks reportedly spent 2 billion USD buying Tabular, it might have been when AWS announced S3 Tables built on Iceberg. But do you know what Apache Iceberg actually is? Or how you could start using it today?

In this tutorial, we will walk through an end-to-end example of writing and reading Iceberg data, while taking a few pitstops to demonstrate Iceberg's selling points.