DevOps & MLOps
8 talks from the 2023 edition.
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Bringing NLP to Production (an end to end story about some multi-language NLP services)
Larissa Haas, Jonathan Brandt
Models in Natural Language Processing are fun to train but can be difficult to deploy. The size of their models, libraries and necessary files can be challenging, especially in a microservice environment. When...
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From notebook to pipeline in no time with LineaPy ▶ Recording 📝 Transcript
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. Unfortunately, your...
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Machine Learning Lifecycle for NLP Classification in E-Commerce ▶ Recording 📝 Transcript
Gunar Maiwald, Tobias Senst
Running machine learning models in a production environment brings its own challenges. In this talk we would like to present our solution of a machine learning lifecycle for the text-based cataloging classification...
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Maximizing Efficiency and Scalability in Open-Source MLOps: A Step-by-Step Approach ▶ Recording 📝 Transcript
Paul Elvers
This talk presents a novel approach to MLOps that combines the benefits of open-source technologies with the power and cost-effectiveness of cloud computing platforms. By using tools such as Terraform, MLflow, and...
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MLOps in practice: our journey from batch to real-time inference ▶ Recording 📝 Transcript
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. In particular, I will focus on the design decisions and open-source tools we built...
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Software Design Pattern for Data Science ▶ Recording 📝 Transcript
Theodore Meynard
Even if every data science work is special, a lot can be learned from similar problems solved in the past. In this talk, I will share some specific software design concepts that data scientists can use to build...
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Staying Alert: How to Implement Continuous Testing for Machine Learning Models ▶ Recording 📝 Transcript
Emeli Dral
Proper monitoring of machine learning models in production is essential to avoid performance issues. Setting up monitoring can be easy for a single model, but it often becomes challenging at scale or when you face...
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The State of Production Machine Learning in 2023 ▶ Recording 📝 Transcript
Alejandro Saucedo
As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our...