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Title & Speakers Event
Nick Jetten – Co-founder/CEO @ Enjins

Productionizing ML in a Scale-up Setting

AI/ML
Martin Jewell – MLOps Engineer @ GetYourGuide

Branch-based Model Experimentation

Andrei Vishniakov – Machine Learning Engineer @ ZenML

Many companies at some point have a need to have an ML Platform to accelerate ML adoption. How can you get one and make sure your team can maintain it in the long run? This talk will give you a practical guide on how to approach ML Platform building: What options do we have, and what are the drawbacks? How do you ensure the stability of your ML Platform in a world where new MLOps tools pop up every week and make your ML Platform usable by Data Scientists?

AI/ML MLOps
Ekin Karabulut – Data Scientist @ Run:ai

In the realm of AI, GPUs play a crucial role in data science projects. Yet, squeezing the most out of them is a challenge, particularly for data scientists with limited resources and time. This talk explores the often-overlooked world of GPU orchestration and allocation with a dash of humour. We’ll address common mistakes like underutilization, static assignments, and resource sharing pitfalls. Introducing Genv, an open-source tool, we’ll transform these challenges into opportunities for seamless and efficient AI workflows.

AI/ML Data Science
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