PyCon: DevOps & MLOps Session List
Bringing NLP to Production (an end to end story about some multi-language NLP services)
Larissa Haas, Jonathan Brandt
How to bring NLP models to production? Following a use case that runs for over 1 year in 10 different languages, this talk will enable you to ask the right questions before starting to deploy NLP services.
From notebook to pipeline in no time with LineaPy
Thomas Fraunholz
The nightmare before data science production: You found a working prototype for your problem using a Jupyter notebook and now it's time to build a production grade solution from that notebook. The good news is, there's finally a cure: The open-source python package LineaPy!
Machine Learning Lifecycle for NLP Classification in E-Commerce
Gunar Maiwald, Tobias Senst
idealo.de presents its MLOps solution and ML lifecycle for product classification
Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach
Paul Elvers
Novel approach to #MLOps combines open-source tech with cloud computing to build scalable, maintainable ML system accessible to ML Engineers & Data Scientists.
MLOps in practice: our journey from batch to real-time inference
Theodore Meynard
I will present the challenges we encountered while migrating an ML model from batch to real-time predictions and how we handled them.
Software Design Pattern for Data Science
Theodore Meynard
I will share some specific software design concepts that can be used by data scientists to build better data products.
Staying Alert: How to Implement Continuous Testing for Machine Learning Models
Emeli Dral
ML monitoring might be easy for a single model, but hard at scale. In this talk, I will introduce the idea of test-based monitoring, and how to standardize data and model checks across models and lifecycle.
The State of Production Machine Learning in 2023
Alejandro Saucedo
Join us at the PyCon DE conference to learn about the current state of production machine learning in the Python ecosystem! We'll cover key principles, frameworks for end-to-end ML lifecycle, best practices, and recommended tools for deployment, security, and scaling.
Filter