PyLadies Amsterdam + MLOps.Community Meetup [in person]

Welcome to a new live get together, this time it is a joint effort with Amsterdam MLOps.community. We'll have a combination of talks, lightning talks and ample time to socialize!

Agenda:
18:00 - Arrival + food
18:20 - Welcome note
18:30 - A tale of machine-learning operations: From organically grown infrastructure, to a mature ML platform by Olivia Stoicescu
19:00 - MLOps: why and how to build end-to-end product teams by Daniel Willemsen
19:30 - Distributed Learning Opportunities and Challenges by Katharine Jarmul
20:00 - Lighting talks, networking and closing

RSVP instructions:

• We have 100 seats in total, where 50 seats are open for you here. If there are no more seats available here, please, sign up here Amsterdam MLOps.community
• Let us know if you want to give a ️lightning talk (first come first serve)
• Let us know if you have any strict dietary restrictions (e.g. vegan)

Talk 1 - A tale of machine-learning operations: From organically grown infrastructure, to a mature ML platform
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.

Speaker
Olivia Stoicescu
https://www.linkedin.com/in/oliviastoicescu/
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.

Talk 2 - MLOps: why and how to build end-to-end product teams
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.

Speaker
Daniel Willemsen
https://nl.linkedin.com/in/dani%C3%ABl-willemsen-baa963a9
Daniel is a machine learning engineer at GoDataDriven, now called Xebia, particularly interested in getting machine learning models from problem to solution in production

Talk 3 - Distributed Learning Opportunities and Challenges
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.

Speaker
Katharine Jarmul
https://de.linkedin.com/in/katharinejarmul
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.

GitHub Repo:
https://github.com/pyladiesams/mlops-event-feb2023

Any questions --> [amsterdam@pyladies.com](mailto:amsterdam@pyladies.com)