This meetup is all about MLOps and it is a joint effort with Amsterdam MLOps Community
Talk 1 - A tale of machine-learning operations: From organically grown infrastructure, to a mature ML platform by Olivia Stoicescu
We are going to walk through the evolution of data and ML infrastructure, taking in the perspectives of various organisations, as they transition from ad-hoc infrastructure to mature ML platforms.
Olivia Stoicescu is a software engineer with 10+ years experience in building data-driven systems, managing MLOps teams and a life-long learner of all things data, ML and Ops.
The presentation can be find here.
Getting machine learning systems to run in production at a large company is hard. MLOps promises to solve this, but has become overwhelming in the amount of tools that are supposed to achieve that. However, just tooling will not solve your problems. A major hurdle that’s often preventing running ML in production is the existence of a handover between data science teams building models and IT teams operating them. Such a handover does not work for ML systems. This talk will show how building end-to-end data science product teams will enable you to run machine learning in production.
Daniel Willemsen is a machine learning engineer at GoDataDriven, now called Xebia, particularly interested in getting machine learning models from problem to solution in production
The presentation can be find here.
You may have heard about federated learning, and are curious how to offer users more privacy while still using data for training. In this talk, you'll learn about the common setups, problems and current solutions in distributed learning; while also considering new opportunities on how distributed learning can offer privacy, security and social benefits.
Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She works as a Principal Data Scientist at Thoughtworks and has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy. She is a passionate and internationally recognized data scientist, programmer, and lecturer and one of the founders of PyLadies.
The presentation can be find here.
This event was set up by @pyladiesams