MLOps & DevOps

4 talks from the 2024 edition.

Beyond Deployment: Exploring Machine Learning Inference Architectures and Patterns ▶ Recording 📝 Transcript

Tim Elfrink

This talk is about setting up robust and scalable machine learning systems for high-throughput real-time predictions and large numbers of users. It is meant for ML engineers and people who work with data and want to...
DDataflow: An open-source end-to-end testing framework for ML pipelines ▶ Recording

Theodore Meynard, Jean Machado

In the realm of machine learning, the complexity of data pipelines often hinders rapid experimentation and iteration. This talk will introduce [DDataflow](https://github.com/getyourguide/DDataFlow), an innovative...
Streamlining Python Development: A Practical Approach to CI/CD with GitHub Actions ▶ Recording 📝 Transcript

Artem Kislovskiy

Crafting code for minimal dependencies and maximum portability is an art. This talk focuses on how continuous integration and delivery ensure project resilience to Python updates and changes in the packaging...
The key to reliability - Testing in the field of ML-Ops ▶ Recording 📝 Transcript

Gunar Maiwald, Tobias Senst

Testing is a de facto standard in modern software development. With increasing awareness that comes with ML-Ops, testing becomes more important for the development and operation of machine learning-based components....