MLOps & DevOps
8 talks from the 2026 edition.
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A minimalist introduction to Ansible ▶ Recording 📝 Transcript
Raniere Silva
[Ansible](https://docs.ansible.com/) is a popular [infrastructure as code](https://en.wikipedia.org/wiki/Infrastructure_as_code) tool for server configuration and software deployment. This tutorial will cover things...
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Empowering Data Scientists with Zero Platform Friction: Deploying Streamlit & Friends in 3 Minutes ▶ Recording
Bernhard Schäfer, Nicolas Renkamp
A data scientist builds a Streamlit or Dash prototype, the business wants to validate it, and the hard parts begin: getting access to live data, making the app available company-wide, and ensuring every user only...
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From Research Models to SLAs: Operationalizing TSFMs with Python ▶ Recording 📝 Transcript
Jeyashree Krishnan, Catarina Filipe
Time series foundation models (TSFMs) such as Chronos, Lag-Llama, TimesFM, and Siemens’ own GTT have shown strong generalization capabilities across diverse forecasting tasks. However, integrating these models into a...
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Holistic Optimization: Implementing "Pipeline-as-a-Trial" HPO with Ray and Cloud Infra ▶ Recording 📝 Transcript
Abdullah Taha
Most hyperparameter optimization (HPO) stops at the model boundary. But what happens when your system relies on a complex chain of steps, a short-horizon model, a long-horizon model, ensembles, postprocesses etc?...
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Learnings Building DevOps as a Software Engineer ▶ Recording 📝 Transcript
Gaweng Tan
When I joined my current company as a software engineer, I encountered a blank slate: no CI/CD pipelines, no deployment infrastructure, barely any monitoring—in short, no software infrastructure at all. This talk...
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Production ML across 2015-2035: A Journey to the Past and the Future ▶ Recording 📝 Transcript
Alejandro Saucedo
This talk is an exciting journey that revisits the past decade of Production Machine Learning from 2015 until now, and provides a pragmatic outlook of the next decade towards 2035. We’ll revisit some of the...
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The Day the Agent Started Lying (Politely) ▶ Recording 📝 Transcript
Asya Melnik
You deploy an agent to automatically route incoming customer support tickets. At first, it is a clear win: response times improve, customers are happier, and support teams finally get some rest. Then time passes....
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When LLMs Are Too Big: Building Cost-Efficient High-Throughput ML Systems for E-Commerce Cataloging ▶ Recording 📝 Transcript
Tobias Senst, Bastian Wandt
E-commerce cataloging at idealo operates at extreme scale: 4.5 billion offers from 50,000+ shops across six countries, with peak ingestion rates of 4.8 million offers per minute. While large language models (LLMs)...