Data Visualization Session List
Detecting drift: how to evaluate and explore data drift in machine learning systems
Emeli Dral
Best Practice, Data Visualization, StatisticsWhen ML model is in production, you might encounter data and prediction drift. How exactly to detect and evaluate it? I'll share in this talk.
Easily build interactive plots and apps with hvPlot
Philipp Rudiger, Maxime Liquet
Data Visualization, Jupyter, ScienceDo you use the .plot() API of pandas or xarray? Do you ever wish it was easier to try out different combinations of the parameters in your data-processing pipeline? Follow this tutorial to learn how to easily build interactive plots and apps with hvPlot.
Efficient data labelling with weak supervision
Maria Mestre
Data Engineering, Data Visualization, Natural Language ProcessingData labelling should not be a waterfall task. Label your data significantly faster with weak supervision (https://github.com/dataqa/dataqa)
Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans
Best Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, ReproducibilityFlexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
How a simple streamlit dashboard will help to put your machine learning model in production
Daniël Willemsen, Welmoet Verbaan
Best Practice, Data Visualization, Predictive ModellingHave you struggled getting your valuable machine learning model into the hands of users? A simple streamlit monitoring dashboard can help!
Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains Datalore Team
Alena Guzharina
Data Visualization, Jupyter, ReproducibilityOvercoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains @Datalore Team Join our talk to discuss setting up environments, working with data, writing code without IDE support, and sharing results, as well as collaboration and reproducibility.
Sankey Plots with Python
Daniel Ringler
Data Visualization, Jupyter, Python fundamentalsSankey Plots in Python? Get an introduction on how and when to use them.
Seeing the needle AND the haystack: single-datapoint selection for billion-point datasets
Jean-Luc Stevens
Big Data, Data Visualization, JupyterBuilding simple custom interactive web dashboards that display millions or billions of samples while giving access to each individual sample.
We know what your app did last summer. Do you? Observing Python applications using Prometheus.
Jessica Greene (she/her), Vanessa Aguilar
Data Visualization, DevOps, PerformanceWe know what your app did last summer. Do you? Join us for this practical & theoretical session if you’re looking to grasp the key concepts of observability, useful metrics, and ensuring operational excellence for your Python applications using Prometheus!
Web based live visualisation of sensor data
Jannis Lübbe
APIs, Data Visualization, Use CaseStreaming sensor data to multiple end devices using FastAPI and websockets.
XAI meets Natural Language Processing
Larissa Haas
Data Visualization, Ethics (Privacy, Fairness,… ), Transparency / InterpretabilityXAI meets NLP - approaches, workarounds and lessons learned while making an NLP project explainable
Your data, your insights: creating personal data projects to (re-)own the data you share
Paula Gonzalez Avalos
Data Visualization, Predictive ModellingYour data, your insights: 3 examples to illustrate how we can apply common data science libraries together with data shared via mobile apps or collected manually to build little data visualization projects that provide unique, contextual and intmiate insights.
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