Try It Out!

Open Tutorial Open JupyterLab

Click here to learn more about the integration with Jupyter.

Note: Open JupyterLab will launch Jupyter in a hosted environment, it may take a few minutes to load.

How It Works

Why Ploomber?

Gentle learning curve

Learning curve.

Get started quickly. Learn advanced features as you progress.

Show me some code

Unlocks fast iterations

Fast iterations.

Data Science is an iterative process. Iterate faster by skipping redundant computations.

Explain me how

Eases transition to production

Transition to production.

Deploy without code changes to a single server, Kubernetes, AWS Batch or Airflow.

See deployment guide

Selected Examples

See examples repository

Selected Presentations

Watch more videos

Recent Posts

Rails allows anyone to build a blog engine in 15 minutes; how would this translate to the Machine Learning development world? This post represents my vision of what a Ruby on Rails for Machine Learning should look like. I’ve been talking to many data practitioners these past few months, from Ploomber users to maintainers of other data tools. A recurrent topic has been the state of the …

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Existing experiment trackers come with a high setup cost. To get one working, you usually have to spin up a database and run a web application. After trying multiple options, I thought that using Jupyter notebooks could be an excellent choice to store experiment results and retrieve them for comparison. This post explains how I use .ipynb files to track experiments without any extra …

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Notebooks are a magnificent tool to explore data, but such a powerful tool can become hard to manage quickly. Ironically, the ability to interact with our data rapidly (modify code cells, run, and repeat) is the exact reason why a notebook may become an obscure entanglement of variables that are hard to understand, even to the notebook’s author. But it doesn’t have to be that way. This …

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