Conferences / PyData
PyData London 2023
Jun 2–4, 2023 · Leonardo Royal Hotel London Tower Bridge, London, United Kingdom
- Conference
- PyData Data science & ML
- Dates
- Jun 2, 2023 – Jun 4, 2023
- Venue
- Leonardo Royal Hotel London Tower Bridge
- Place
- London, United Kingdom
- Format
- In person
- Status
- Past
- Attendance
- —
- Website
- pydata.org
- Recorded
- 9 sponsors · 66 speaker appearances
- Notes
- Sponsor names identified from logo links/filenames (nstack.com link shown with the Datapane logo). Speakers: all sessions in the pretalx schedule export (self-hosted pretalx, retrieved via the Wayback Machine); affiliations parsed from pretalx bios, null where not stated. Talk URLs point to the now-defunct pretalx host. Ticket prices not found.
Ticket prices
No ticket prices recorded
Sponsors 9
Title / headline 1
- Tesco · Diamond
Platinum 5
Speakers and talks 66
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Antonio Campello | Digital Science | Keynote | Keynote: Using data science for social good | — |
| Ines Montani | Explosion | Keynote | Keynote: Large Language Models: From Prototype to Production | — |
| Lisa Carpenter | Digital Futures | Keynote | Keynote: Using data science for social good | — |
| Lisa Carpenter | Digital Futures | Keynote | Teach data science better (a discussion) | — |
| Adam Hill | — | Talk | Building a data science solution for an NGO when you don’t know what infrastructure it will run on: a case study predicting tutor supply and demand mismatch | — |
| Ade Idowu | — | Tutorial | Hands-on Intro to developing Explainability for Recommendation Systems | — |
| Alberto Labarga | — | Tutorial | Building an End-to-End Open-Source Modern Data Platform for Biomedical Data | — |
| Alexander Hendorf | — | Talk | ✨ fastAPI facts we wish we'd known beforehand. Spoiler: It's not about getting started. | — |
| Arthur Turrell | — | Talk | Python for the Public Sector: How data science is being put to work for the public good | — |
| Ben Auffarth | — | Talk | ChatGPT, LLMs, and the future of data science | — |
| Carsten Frommhold | Datadrivers | Talk | Mastering Great Expectations: Ensuring Data Quality in Your Data Pipelines. | — |
| Cesc Cunillera | Tesco | Talk | Introduction to RL for pricing problems | — |
| Cheuk Ting Ho | — | Talk | Unconference: Is it GDPR 2.0? What is CRA and how can it affect you and OSS in general | — |
| Cheuk Ting Ho | — | Talk | Driving down the Memray lane - Profiling your data science work | — |
| Debayan Das | MiiCare | Talk | The Sound of Your Footsteps is a Digital Biomarker | — |
| Eoin O'Flanagan | Databricks | Tutorial | Delta Lake 101: How many water metaphors does it take to describe data? | — |
| Florian Jacta | Groupe Les Mousquetaires | Talk | How to build stunning Data Science Web applications in Python | — |
| Frederik Hvilshøj | Encord | Talk | The Future of MLOps: Embedding Active Learning into Your ML Model Development Pipelines | — |
| Gabriel Harris | NIQ | Tutorial | Bring best practices to your messy data science team! | — |
| Giles Weaver | — | Talk | Pandas 2, Dask or Polars? Quickly tackling larger data on a single machine | — |
| Habeeb Shopeju | Thomson Reuters | Tutorial | Martial Arts Meets Machine Learning: Recognizing Judo Throws with MMAction2 | — |
| Holly Smith | — | Tutorial | Delta Lake 101: How many water metaphors does it take to describe data? | — |
| Ian Ozsvald | — | Talk | Discussing Higher Performance Python (Birds of a Feather session) | — |
| Ian Ozsvald | — | Talk | Pandas 2, Dask or Polars? Quickly tackling larger data on a single machine | — |
| Ian Ozsvald | — | Talk | Executives at PyData | — |
| India Kerle | — | Talk | Building a skills extraction library using NLP tools | — |
| Jack Vines | Nesta | Talk | Building a skills extraction library using NLP tools | — |
| James Powell | Don't Use This Code | Talk | Grouped Weighted Summary Statistics in pandas… and Other Things | — |
| James Schofield | Kaluza | Talk | Large scale agent-based simulations: how to do it right, and how we used one to optimise fibre broadband rollout across the UK | — |
| Jay Chia | — | Talk | "Unstructured" terabyte-scale textual data processing in a distributed cluster | — |
| Jim Dowling | KTH Royal Institute of Technology | Tutorial | From zero to a working ML system with only Python, free serverless services and FTI pipelines | — |
| Johannes Kolbe | Fraunhofer Institute | Talk | Robot Holmes and the Vision-Language Murder Mysteries | — |
| John Carney | Netacea | Talk | Data work across Industries - Discussion | — |
| jonny edwards | — | Tutorial | An Introduction to Polars | — |
| Konstantin Lopukhin | Zyte | Talk | Web Data Extraction with Deep Learning | — |
| Laszlo Sragner | — | Talk | Code Smells in Data Science: What can we do about them? | — |
| Laszlo Sragner | — | Talk | Software Engineering Practices in Data Science | — |
| Leo Anthias | — | Talk | Building end-to-end internal analytics products using Python and open-source | — |
| Lisa Carpenter | Digital Futures | Tutorial | Entering the Forest with TensorFlow - An intro | — |
| Liz Gallagher | Nesta | Talk | Building a skills extraction library using NLP tools | — |
| Mark Pinkerton | Risilience | Talk | Green software - building sustainable Python data analytics | — |
| Marysia Winkels | GoDataDriven | Talk | Data Storytelling through Visualization | — |
| Mate Timar | — | Tutorial | From Passive to Active: Exploring the Benefits of Active Learning in Data Science | — |
| Matteo Latinov | Yanmar R&D Europe | Talk | Software Engineering Practices in Data Science | — |
| Matthew Rocklin | — | Talk | New Developments in Pandas and Dask Dataframes | — |
| Meder Kamalov | Features and Labels | Talk | Unleashing the Power of dbt and Python for Modern Data Stack | — |
| Natan Mish | Zimmer Biomet | Talk | Event Driven Machine Learning | — |
| Nischal | Scoutbee GmbH | Talk | Language Models for Music Recommendation | — |
| Pedro Tabacof | Intercom | Talk | Deploying Real-Time Machine Learning Models Using Serverless AWS | — |
| Petros Syntelis | University of St Andrews | Talk | Causal modelling of agent-customer pairing outcomes to optimise call centre performance | — |
| Raghotham S | PayPal | Talk | Language Models for Music Recommendation | — |
| Raphaël Lüthi | Groupe Mutuel | Talk | Getting from data to insights with powerful XAI & DataViz open-source tool (inc. diving deep into shap’s TreeExplainer) | — |
| Sagar Mishra | Oorjaa | Tutorial | sktime - python toolbox for time series: time series classification, regression, clustering, with modular time series distances and kernels | — |
| Sam Joseph | — | Talk | The 11 Types of Comedy and Large Language Models (LLMs) | — |
| Samuel Colvin | Pydantic | Talk | Garbage in -> Pydantic -> you're golden! | — |
| Steve Goodman | Tide | Talk | From correlations to causality in machine learning– a gentle guide to causal inference | — |
| Tanay Agrawal | Curl HG | Tutorial | A dive into Hyperparameter Optimization | — |
| Theodore Meynard | GetYourGuide | Talk | MLOps in practice: our journey from batch to real-time inference | — |
| Theodore Meynard | GetYourGuide | Tutorial | MLflow workshop | — |
| Tristan West | Ocado Technology | Talk | Large scale agent-based simulations: how to do it right, and how we used one to optimise fibre broadband rollout across the UK | — |
| Valerio Bonometti | — | Talk | Autoencoders for Time Series Clustering | — |
| Valerio Maggio | Anaconda | Talk | Actionable Machine Learning in the Browser with PyScript | — |
| vincenzo crescimanna | Tesco | Talk | Autoencoders for Time Series Clustering | — |
| William Dealtry | — | Talk | Serverless Python Analytics at Petabyte scale using ArcticDB | — |
| Yannick Wolff | Sicara | Talk | The Opinionated Python Stack I chose for my Company’s ML Projects and how I bundled it in a Project Generator | — |
| Zhaozhi Qian | — | Talk | Synthetic data: what is it and why do we need it? | — |