Conferences / PyData
PyData Seattle 2023
Apr 26–28, 2023 · Microsoft Conference Center (Building 33), Redmond, Redmond, WA, United States
- Conference
- PyData Data science & ML
- Dates
- Apr 26, 2023 – Apr 28, 2023
- Venue
- Microsoft Conference Center (Building 33), Redmond
- Place
- Redmond, WA, United States
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- pydata.org
- Recorded
- 27 sponsors · 85 speaker appearances
- Notes
- Branded PyData Seattle but held on the Microsoft campus in Redmond, WA. Tutorials Apr 26, talks Apr 27-28. Tickets sold via Tito; prices not recovered. Speakers: all speakers from the archived pretalx speaker list; role 'tutorial' = 90-minute sessions on the archived schedule; affiliations only where the pretalx bio states one; video URLs matched from the PyData Seattle 2023 YouTube playlist.
Ticket prices
No ticket prices recorded
Sponsors 27
Title / headline 5
- Anaconda · Diamond
- Delta Lake · Diamond
- Microsoft · Host
- Posit · Diamond
- Union.ai · Diamond
Platinum 3
Community 13
Other 2
- Costanoa Ventures · PyData Seattle Social Sponsored by
- Tryolabs · Supporting
Speakers and talks 85
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Holden Karau | Netflix | Keynote | Keynote: Distributed Computing 4 Kids -- with Spark (and guest appearances from Ray and Dask) | Video |
| Katrina Riehl | Snowflake | Keynote | Keynote: Scientific Computing and the Gateway to Open Source | Video |
| Peter Wang | Anaconda | Keynote | Keynote: Peter Wang | Video |
| Travis Oliphant | — | Keynote | Keynote: Travis Oliphant | Video |
| Aditya Lahiri | UC San Diego | Speaker | Explaining Explainable AI tools : Issues, Pitfalls and Cautionary tails | — |
| Akshay Bahadur | Carnegie Mellon University | Speaker | Indian Sign Language Recognition(ISLAR) | Video |
| Alan Descoins | Tryolabs | Panelist | Panel: The living nature of data: exploring the Lifecycle and Management of Data at Scale | — |
| Alexander Shirkov | Amazon Web Services | Tutorial | Leveraging Text, Images, and the Kitchen Sink to solve complex ML problems in a few lines of code with AutoGluon | Video |
| Andreas C Mueller | Microsoft | Tutorial | Automated Machine Learning & Tuning with FLAML | Video |
| Anindya Saha | Lyft | Tutorial | Being well informed: Building a ML Model Observability pipeline | Video |
| Anthony Holten | Interos | Tutorial | Fugue: Porting Existing Python and Pandas Code to Spark, Dask, and Ray | Video |
| Bernease Herman | WhyLabs | Speaker | Monitoring in the era of Generative AI, LLVMs, and embeddings – why truly scalable approaches matter | Video |
| Carl Kadie | — | Speaker | A Perfect, Infinite-Precision, Game Physics in Python | Video |
| Carl Kadie | — | Speaker | Nine Rules for Writing Python Extensions in Rust | Video |
| Chengyin Eng | Databricks | Speaker | Scaling data workloads using the best of both worlds: pandas and Spark | Video |
| Chi Wang | Microsoft | Tutorial | Automated Machine Learning & Tuning with FLAML | Video |
| Darren Vengroff | — | Tutorial | Introduction to Working with U.S. Census Data in Python | Video |
| David Aronchick | Expanso | Panelist | Panel: The living nature of data: exploring the Lifecycle and Management of Data at Scale | — |
| David Qiu | Amazon Web Services | Speaker | Jupyter AI — Bringing Generative AI to Jupyter | Video |
| Eduardo Apolinario | Union.ai | Tutorial | Flyte: Robust and End-to-End Cloud Native Machine Learning & Data Processing Platform | Video |
| Eloisa Elias Tran | — | Speaker | The Continuous Improvement Journey: How Data Science Complements the Six Sigma Methodology in Manufacturing | Video |
| Eloisa Elias Tran | — | Panelist | Diversity Panel: Allyship is a journey, not a destination | Video |
| Eugene Ciurana | Triple | Speaker | You Want to Buy This - Particle Swarm Classification for Next-Gen Recommendation Engines | Video |
| Fabiana Clemente | YData | Speaker | The Importance of Synthetic Data in Data-Centric AI | Video |
| Fabiana Clemente | YData | Panelist | Panel: The living nature of data: exploring the Lifecycle and Management of Data at Scale | — |
| Federico Garza Ramirez | Nixtla | Speaker | Quantifying Uncertainty in Time Series Forecasting with Conformal Prediction | Video |
| Florian Jacta | Taipy | Speaker | How to build stunning Data Science Web applications in Python with Taipy | Video |
| Franz Kiraly | — | Tutorial | skbase - a workbench for creating scikit-learn like parametric objects and libraries | Video |
| Gil Forsyth | — | Speaker | Ibis: Because SQL is everywhere but you don't want to use it | Video |
| Hamel Husain | fast.ai | Panelist | Panel: “Building a Stronger Open Source Python Data Community: Trends, Gaps, and Collaborative Contributions” | Video |
| Han Wang | Lyft | Speaker | How to incrementally scale existing workflows on Spark, Dask or Ray? | Video |
| Hyukjin Kwon | Databricks | Speaker | Scaling data workloads using the best of both worlds: pandas and Spark | Video |
| Ilya Katsov | Grid Dynamics | Speaker | Scaling MLOps to support dozens of analytics teams | — |
| J.J. Allaire | — | Speaker | Publishing Jupyter Notebooks with Quarto | Video |
| Jia Yu | Wherobots | Speaker | Geo-Unleashed: How Apache Sedona is Revolutionizing Geospatial Data Analysis | Video |
| JIm Dowling | Hopsworks | Tutorial | Build a production ML system with only Python on free serverless services | Video |
| Jim Hibbard | Databricks | Tutorial | Building Reliable, Open Lakehouses with Delta Lake | Video |
| Joe Cheng | Posit | Speaker | Shiny: Data-centric web applications in Python | Video |
| Jon Mease | Hex | Speaker | Scaling Altair visualizations with VegaFusion | Video |
| Jonathan Bechtel | DSML Research | Tutorial | skbase - a workbench for creating scikit-learn like parametric objects and libraries | Video |
| Juanita Gomez | UC Santa Cruz | Panelist | Panel: “Building a Stronger Open Source Python Data Community: Trends, Gaps, and Collaborative Contributions” | Video |
| Jules S. Damji | Anyscale | Tutorial | Introduction to Ray for distributed and machine learning applications in Python | Video |
| Jun Liu | Lyft | Speaker | How to incrementally scale existing workflows on Spark, Dask or Ray? | Video |
| Kamil Kaczmarek | Anyscale | Speaker | Emerging Open Source Tech Stack for Large Language Models (LLMs) with Ray AI Runtime | — |
| Katrina Riehl | Snowflake | Panelist | Panel: “Building a Stronger Open Source Python Data Community: Trends, Gaps, and Collaborative Contributions” | Video |
| Ketan Umare | Union.ai | Speaker | The Python Data Ecosystem: Navigating a fragmented landscape. | Video |
| Kevin Kho | — | Tutorial | Fugue: Porting Existing Python and Pandas Code to Spark, Dask, and Ray | Video |
| Koushik Krishnan | — | Speaker | Notebooks as Serverless Functions | Video |
| Krishi Sharma | KUNGFU.AI | Speaker | Trust Fall: Hidden Gems in MLFlow that Improve Experiment Reproducibility | Video |
| Krishi Sharma | KUNGFU.AI | Speaker | Plant a Touch-Me-Not: Train Models Without Anyone Touching Your Data with Flower | Video |
| Leland McInnes | Tutte Institute for Mathematics and Computing | Speaker | Data Mapping for Data Exploration | Video |
| Leo Anthias | Datapane | Speaker | Replacing Proprietary SaaS with Open-Source: Building a Marketing Analytics Web App with Python | Video |
| Li Jiang | Microsoft | Tutorial | Automated Machine Learning & Tuning with FLAML | Video |
| Lucas Durand | TD Securities | Tutorial | Building an Interactive Network Graph to Understand Communities | — |
| Madison Swain-Bowden | Automattic | Speaker | Managing a search engine for over 600 million openly licensed media records | Video |
| Mary Grace Moesta | Databricks | Speaker | MLOps Deployment Patterns with Delta Lake and MLflow | Video |
| Max Mergenthaler | Nixtla | Speaker | Quantifying Uncertainty in Time Series Forecasting with Conformal Prediction | Video |
| Michael Byington | INGU Solutions | Speaker | U-Net-style neural networks for feature identification in 1D time-series: applications in pipeline inspection, medicine, and more | Video |
| Misha Desai | Microsoft | Tutorial | Automated Machine Learning & Tuning with FLAML | Video |
| Nate Stemen | — | Speaker | Growing the open source quantum ecosystem | Video |
| Naty Clementi | Coiled | Speaker | Open Source meets Enterprise: The right way. | Video |
| Nick Erickson | Amazon Web Services | Tutorial | Leveraging Text, Images, and the Kitchen Sink to solve complex ML problems in a few lines of code with AutoGluon | Video |
| Nidhin Pattaniyil | Walmart | Tutorial | Building a Search Engine | Video |
| Pablo Alfaro | Tryolabs | Speaker | Untangling the complexity of demand forecasting models: building a Market Simulator | Video |
| Peter Vidos | Vizzu | Tutorial | Hands-on intro of ipyvizzu-story - a new, open-source charting tool to build, create and share animated data stories with Python in Jupyter | Video |
| Phillip Cloud | Voltron Data | Speaker | Ibis: Because SQL is everywhere but you don't want to use it | Video |
| Pierre Brunelle | Noteable | Speaker | Combining IPython with Open Source Papermill, Origami, and Genai to enhance your Jupyter Notebook experience | Video |
| Qingyun Wu | Penn State University | Tutorial | Automated Machine Learning & Tuning with FLAML | Video |
| Rajeev Prabhakar | Lyft | Tutorial | Being well informed: Building a ML Model Observability pipeline | Video |
| savin goyal | Outerbounds | Speaker | Enterprise-grade Full Stack ML Platform: why human-centricity matters? | Video |
| Shivay Lamba | — | Speaker | Building Machine Learning Microservices & MLOps using Union ML | Video |
| Sophia Yang | Anaconda | Speaker | Python Anytime, Anywhere with Anaconda Notebooks | Video |
| Stefan Krawczyk | — | Panelist | Panel: “Building a Stronger Open Source Python Data Community: Trends, Gaps, and Collaborative Contributions” | Video |
| Sumedh Datar | — | Speaker | Deep Learning Model Interpretability for Computer Vision based Models | Video |
| Thiagarajan Ramakrishnan | Dell Technologies | Speaker | Enterprise-grade Full Stack ML Platform: why human-centricity matters? | Video |
| Ties de Kok | University of Washington | Tutorial | Going beyond ChatGPT: an introduction to prompt engineering and LLMs | Video |
| Timothy Chan, PhD | Statsig | Speaker | Experimentation and the gold standard of data champions | Video |
| Tom Drabas | Voltron Data | Speaker | From prototype to deployment: Increase productivity and simplify data operations in Python | Video |
| Tracy Teal | Posit | Speaker | It's not just code: managing an open source project | Video |
| Trent Hauck | WHERE TRUE Technologies | Speaker | Python in Bioinformatics | Video |
| Vincent Gosselin | Taipy | Speaker | How to build stunning Data Science Web applications in Python with Taipy | Video |
| Yee Tong | Union.ai | Speaker | The Python Data Ecosystem: Navigating a fragmented landscape. | Video |
| Ying-Jung Chen | — | Speaker | Let’s program to fight the impacts of climate change! | Video |
| Yucheng Low | XetHub | Panelist | Panel: The living nature of data: exploring the Lifecycle and Management of Data at Scale | — |
| Zander Matheson | Bytewax | Panelist | Panel: “Building a Stronger Open Source Python Data Community: Trends, Gaps, and Collaborative Contributions” | Video |
Sources
- https://pydata.org/seattle2023/
- https://pydata.org/seattle2023/about/
- https://pydata.org/seattle2023/venue/
- https://pydata.org/seattle2023/keynotes/
- https://pydata.org/seattle2023/sponsors/
- https://ti.to/pydata/pydata-seattle-2023
- https://www.youtube.com/playlist?list=PLGVZCDnMOq0q81_-rt5jzJ--ZEgcNArKb
- https://web.archive.org/web/20240917193820/https://seattle2023.pydata.org/cfp/speaker/
- https://web.archive.org/web/20250708103954/https://seattle2023.pydata.org/cfp/schedule/nojs