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Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit
Antoine Toubhans
Best Practice, Computer Vision, Data Engineering, Data Visualization, Development Methods, Reproducibility

Flexible ML Experiment Tracking System for Python Coders with DVC and Streamlit

Introduction to MLOps with MLflow
Tobias Sterbak
Best Practice, Predictive Modelling, Reproducibility

Learn the basics of MLops with MLflow to manage the machine learning life-cycle.

JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python
Jeremy Tuloup
Jupyter, Reproducibility, Use Case

JupyterLite is a Jupyter distribution that runs entirely in the web browser, backed by in-browser language kernels such as the WebAssembly powered Pyodide kernel. JupyterLite enables data science and interactive computing with the PyData scientific stack, directly in the browser.

Making MLOps uncool again
David
Best Practice, Development Methods, Reproducibility

In this workshop, we will learn what it means and how to build an "MLOps workflow" by extending the power of Git and GitHub with open-source tools.

Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrains Datalore Team
Alena Guzharina
Data Visualization, Jupyter, Reproducibility

Overcoming 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.

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