https://pyvideo.org/2023-05-10T00:00:00+00:002023-05-10T00:00:00+00:002023-05-10T00:00:00+00:00Luis Lopeztag:pyvideo.org,2023-05-10:/jupytercon-2023/its-live-jupyter-and-cloud-native-formats-to-map-climate-change.html

<h3>Description</h3><p>ITS_LIVE ( <a class="reference external" href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a> ) is a NASA project that produces low latency, global glacier flow and elevation change datasets. The size and complexity of this data makes its distribution and use a challenge. To address these problems, ITS_LIVE was built for modern cloud-optimized data formats and includes …</p>

<h3>Description</h3><p>ITS_LIVE ( <a class="reference external" href="https://its-live.jpl.nasa.gov/">https://its-live.jpl.nasa.gov/</a> ) is a NASA project that produces low latency, global glacier flow and elevation change datasets. The size and complexity of this data makes its distribution and use a challenge. To address these problems, ITS_LIVE was built for modern cloud-optimized data formats and includes easy-to-use Jupyter notebooks for data access and visualization.</p> <p>This presentation will show how ITS_LIVE uses the Pangeo stack to generate Zarr data cubes that make fast access possible without the need of back-end services. We will also delve into our data access strategy and how we leverage and enhanced the Jupyter ecosystem by implementing native map projections and services in ipyleaflet to visualize big geospatial data in a matter of seconds.</p> 2023-05-10T00:00:00+00:002023-05-10T00:00:00+00:00Loïc Estèvetag:pyvideo.org,2023-05-10:/jupytercon-2023/leveraging-the-jupyter-ecosystem-to-create-and-run-the-machine-learning-in-python-mooc.html

<h3>Description</h3><p>We, a team of scikit-learn core developers and contributors, created the &quot;Machine Learning in Python in scikit-learn&quot; MOOC (Massive Open Online Course) in 2021 with the goal of making it accessible to an audience without a strong technical background.</p> <p>Since then, we have run three sessions of the MOOC …</p>

<h3>Description</h3><p>We, a team of scikit-learn core developers and contributors, created the &quot;Machine Learning in Python in scikit-learn&quot; MOOC (Massive Open Online Course) in 2021 with the goal of making it accessible to an audience without a strong technical background.</p> <p>Since then, we have run three sessions of the MOOC, with an average of roughly 10,000 registered participants, and have reused the material for scikit-learn courses in a variety of settings, for example Python conference tutorials, remote scikit-learn training and in-person university courses.</p> <p>In this talk, we will describe how we leveraged tools within the Jupyter ecosystem to develop the course material and teach it, in particular: - JupyterBook and Jupytext to develop the material in a convenient and collaborative fashion - JupyterHub to give learners a zero-install live environment during the MOOC session - Binder for convenient fall-back for tricky installation issues together with its integration into JupyterBook</p> <p>We will also share the lessons we learned along the way while developing the material, running the MOOC and teaching the material.</p> <p>We will conclude with some of our ideas to improve the course, for example: - using Jupyterlite in our JupyterBook setup and potentially replace our JupyterHub in the longer term. Towards this goal, we already have started investigating issues we found in Pyodide scipy and scikit-learn packages - moving away from classic notebook to Retrolab - moving to jupyterlab-myst to better support MyST markdown inside notebooks and get rid of our custom scripts to genenerate HTML admonitions</p> <p>The content of the course is available under a CC-BY license at <a class="reference external" href="https://inria.github.io/scikit-learn-mooc">https://inria.github.io/scikit-learn-mooc</a> and the associated repository at: <a class="reference external" href="https://github.com/inria/scikit-learn-mooc">https://github.com/inria/scikit-learn-mooc</a>. The MOOC is available at: <a class="reference external" href="https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/">https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/</a>.</p>