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12 talks from the 2023 edition.
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A concrete guide to time-series databases with Python ▶ Recording 📝 Transcript
Heiner Tholen, Ellen König
We evaluated time-series databases and complementary services to stream-process sensor data. In this talk, our evaluation will be presented. The final implementation will be shown, alongside python-tools we’ve built...
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Cooking up a ML Platform: Growing pains and lessons learned ▶ Recording 📝 Transcript
Cole Bailey
What is a ML platform and do you even need one? When should you consider investing in your own ML platform? What challenges can you expect building and maintaining one? Tune in and discover (some) answers to these...
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Delivering AI at Scale ▶ Recording 📝 Transcript
Severin Schmitt, Anna Achenbach, Thorsten Kranz
Everybody knows our yellow vans, trucks and planes around the world. But do you know how data drives our business and how we leverage algorithms and technology in our core operations? We will share some “behind the...
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Dynamic pricing at Flix ▶ Recording 📝 Transcript
Amit Verma
In the talk we give a brief overview of how we use Dynamic Pricing to tune the prices for rides based on demand, time of purchase, unexpected events strike etc., and other criteria to fulfil our business requirements.
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Enabling Machine Learning: How to Optimize Infrastructure, Tools and Teams for ML Workflows
Yann Lemonnier
In this talk, we will explore the role of a machine learning enabler engineer in facilitating the development and deployment of machine learning models. We will discuss best practices for optimizing infrastructure...
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Exploring the Power of Cyclic Boosting: A Pure-Python, Explainable, and Efficient ML Method
Felix Wick
We have recently open-sourced a pure-Python implementation of Cyclic Boosting, a family of general-purpose, supervised machine learning algorithms. Its predictions are fully explainable on individual sample level,...
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Have your cake and eat it too: Rapid model development and stable, high-performance deployments
Christian Bourjau, Jakub Bachurski
At the boundary of model development and MLOps lies the balance between the speed of deploying new models and ensuring operational constraints. These include factors like low latency prediction, the absence of...
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How Python enables future computer chips ▶ Recording 📝 Transcript
Tim Hoffmann
At the semiconductor division of Carl Zeiss it's our mission to continuously make computer chips faster and more energy efficient. To do so, we go to the very limits of what is possible, both physically and...
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How to build observability into a ML Platform ▶ Recording 📝 Transcript
Alicia Bargar
As machine learning becomes more prevalent across nearly every business and industry, making sure that these technologies are working and delivering quality is critical. In her talk, Alicia will discuss the...
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Large Scale Feature Engineering and Datascience with Python & Snowflake ▶ Recording 📝 Transcript
Michael Gorkow
[Snowflake](https://www.snowflake.com/en/) as a data platform is the core data repository of many large organizations. With the introduction of Snowflake's [Snowpark for...
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The Spark of Big Data: An Introduction to Apache Spark ▶ Recording 📝 Transcript
Pasha Finkelshteyn
Get ready to level up your big data processing skills! Join us for an introductory talk on Apache Spark, the distributed computing system used by tech giants like Netflix and Amazon. We'll cover PySpark DataFrames...
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Use Spark from anywhere: A Spark client in Python powered by Spark Connect ▶ Recording 📝 Transcript
Martin Grund
Over the past decade, developers, researchers, and the community have successfully built tens of thousands of data applications using Spark. Since then, use cases and requirements of data applications have evolved:...