A concrete guide to time-series databases with Python
Heiner Tholen, Ellen König

A concrete guide to time-series databases with Python - how to choose the right time-series database for your application.

Cooking up a ML Platform: Growing pains and lessons learned
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? Join my talk at PyData to hear how we are cooking up our own ML platform at @Delivery Hero!

Delivering AI at Scale
Severin Schmitt, Anna Achenbach, Thorsten Kranz

Unbelievable tricks to integrate AI into a company with 600k colleagues – experts are shocked!” Learn about Deutsche Post DHL Group’s journey towards a Data-Driven company, with Use Cases, technology details and code snippets #yournextcareerstep #ai #datascience #forecasting

Dynamic pricing at Flix
Amit Verma

How Flixbus designed dynamic pricing strategy according to market demands

Enabling Machine Learning: How to Optimize Infrastructure, Tools and Teams for ML Workflows
Yann Lemonnier

Join us for a deep dive into machine learning enabler engineering! Discover how to optimize infrastructure, tools and teams for ML workflows and reduce time to deployment. Get practical tips and insights for successful projects from an expert in the field. #MLenabler #optimizingM

Exploring the Power of Cyclic Boosting: A Pure-Python, Explainable, and Efficient ML Method
Felix Wick

We just open-sourced Cyclic Boosting, a pure-Python ML algorithm that's explainable, accurate, robust, easy to use, and fast! Learn more in our presentation #CyclicBoosting #MachineLearning #OpenSource

Have your cake and eat it too: Rapid model development and stable, high-performance deployments
Christian Bourjau, Jakub Bachurski

Python's data science tools are fantastic for data exploration and model development, but the price is often a slow and difficult deployment. Join us to find out what tools we have developed to have the cake and eat it too!

How Python enables future computer chips
Tim Hoffmann

Learn how we adopted Python to build the computer chips of the future

How to build observability into a ML Platform
Alicia Bargar

Check out Shopify's talk on how to build observability into a #machinelearing platform. They'll share key learnings on how to track model performance, catch unexpected behaviour & how observability could work with large language models and Chat AIs

Large Scale Feature Engineering and Datascience with Python & Snowflake
Michael Gorkow

Learn how Snowpark for Python enables large scale feature engineering and data science!

The Spark of Big Data: An Introduction to Apache Spark
Pasha Finkelshteyn

Spark your big data skills! Learn Apache Spark basics: data frames, SQL APIs, and merging data for Python devs new to big data & tech explorers. Don't miss out! #ApacheSpark #BigData #Python

Use Spark from anywhere: A Spark client in Python powered by Spark Connect
Martin Grund

Check out how to participate in the extension of Spark Connect to bring the power of Spark everywhere!