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From Jupyter Notebook to DVC pipeline for reproducible ML experiments
In this guide we will take a Jupyter Notebook and use Papermill to turn it into a simple, one-stage DVC pipeline.
Rob de Wit
Oct 24, 2022 • 9 min read
Preventing Stale Models in Production
We're going to look at how you can prevent stale models from remaining in production when the data starts to differ from the training data.
Mar 31, 2022 • 7 min read
Running Collaborative Experiments
Sharing experiments with teammates can help you build models more efficiently.
Dec 13, 2021 • 4 min read
Don't Just Track Your ML Experiments, Version Them
ML experiment versioning brings together the benefits of traditional code versioning and modern day experiment tracking, super charging your ability to reproduce and iterate on your work.
Dec 07, 2021 • 4 min read
Adding Data to Build a More Generic Model
You can easily make changes to your dataset using DVC to handle data versioning. This will let you extend your models to handle more generic data.
Oct 05, 2021 • 7 min read
Using Experiments for Transfer Learning
You can work with pretrained models and fine-tune them with DVC experiments.
Aug 24, 2021 • 12 min read
Tuning Hyperparameters with Reproducible Experiments
Using DVC, you'll be able to track the changes that give you an ideal model.
Jul 19, 2021 • 8 min read
Git Custom References for ML Experiments
In DVC 2.0, we’ve introduced a new feature set aimed at simplifying the versioning of lightweight ML experiments. In this post, we’ll dive into how exactly these new experiments work.
Apr 19, 2021 • 6 min read
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