MLOps & DevOps
14 talks from the 2025 edition.
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Building a Self-Hosted MLOps Platform with Kubernetes ▶ Recording 📝 Transcript
Josef Nagelschmidt
Many managed MLOps platforms, while convenient, often fall short in providing flexibility, requiring complex integrations, and causing vendor lock-in. In this talk, we’ll share our experience transitioning from...
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Data as (Python) Code ▶ Recording 📝 Transcript
Francesco Calcavecchia
In contemporary data-driven environments, the seamless integration of data into automated workflows is paramount. The reliability of automation, however, is constantly threatened by breaking changes in the source...
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Design, Generate, Deploy: Contract-First with FastAPI ▶ Recording 📝 Transcript
Dr. Evelyne Groen, Kateryna Budzyak
This talk explores a contract-first approach to API development using the OpenAPI generator, a powerful tool for automating API generation from a standardized specification. We will cover (1) what would you need to...
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GitMLOps – How we are managing 100+ ML pipelines in AWS SageMaker ▶ Recording 📝 Transcript
Bogdan Girman
Scaling machine learning pipelines is no small feat - especially when you’re managing over 100 of them on AWS SageMaker. In this talk, I’ll take you behind the scenes of how our team at idealo built a Git-based MLOps...
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Going Global: Taking code from research to operational open ecosystem for AI weather forecasting ▶ Recording 📝 Transcript
Jesper Dramsch
When I was hired as a Scientist for Machine Learning, experts said ML would never work in weather forecasting. Nowadays, I get to contribute to Anemoi, a full-featured ML weather forecasting framework used by...
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Guardians of the Code: Safeguarding Machine Learning Models in a Climate Tech World ▶ Recording 📝 Transcript
Doreen Sacker
LLMs, Machine learning and AI are everywhere, yet their security is often overlooked, leaving your systems vulnerable to serious attacks. What happens when someone tampers with your model’s input, poisons your...
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Instrumenting Python Applications with OpenTelemetry ▶ Recording 📝 Transcript
Mika Naylor, Emily Woods
Observability is challenging and often requires vendor-specific instrumentation. Enter OpenTelemetry: a vendor-agnostic standard for logs, metrics, and traces. Learn how to instrument Python applications with...
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Oh, no! Users love my GenAI-Prototype and want to use it more. ▶ Recording 📝 Transcript
Thomas Prexl, Frank Rust
Demos and prototypes for generative AI (GenAI) projects can be quickly created with tools like Streamlit, offering impressive results for users within hours. However, scaling these solutions from prototypes to robust...
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Scaling Python: An End-to-End ML Pipeline for ISS Anomaly Detection with Kubeflow ▶ Recording 📝 Transcript
Christian Geier, Henrik Sebastian Steude
Building and deploying scalable, reproducible machine learning pipelines can be challenging, especially when working with orchestration tools like Slurm or Kubernetes. In this talk, we demonstrate how to create an...
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Size matters: Inspecting Docker images for Efficiency and Security ▶ Recording 📝 Transcript
Irena Grgic
Inspecting Docker images is crucial for building secure and efficient containers. In this session, we will analyze the structure of a Python-based Docker image using various tools, focusing on best practices for...
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Streamlining Python deployment with Pixi: A Perspective from production ▶ Recording 📝 Transcript
Dennis Weyland
In our quest to improve Python deployments, we explored Pixi, a tool designed to enhance dependency management within the Conda ecosystem. This talk recounts our experience integrating Pixi into a setup used in...
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Why E.ON Loves Python ▶ Recording 📝 Transcript
Christer Friberg
Join me as I share my 20-year journey with Python and its pivotal role at E.ON. Discover how we transitioned fully to Python, streamlined our development framework, and embraced MLOps principles. Learn about some of...
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Writing reliable software while depending on hazardous APIs ▶ Recording 📝 Transcript
Romain Dorgueil
As we develop business critical software, we often need to rely on external APIs to get the job done. And all services are not born equal: although the ideal world would provide well operated APIs with over-met...
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You don’t think about your Streamlit app optimization until you try to deploy it to the cloud ▶ Recording 📝 Transcript
Darya Petrashka
Building Streamlit apps is easy for Data Scientists - but when it’s time to deploy them to the cloud, challenges like slow model loading, scalability, and security can become major hurdles. This talk bridges two...