In this tutorial, we will discuss how can we achieve reproducible data pipelines and research while keeping track of the experiments that lead to production Machine Learning models. We will go over all the popular tools available and do a small demo of how we can use these tools (e.g., DVC, Pachiderm, Neptune, Comet, Weights & Biases and MLFlow) to get a seamless workflow with a good balance between production and experimentation.
If you enjoyed the content of this post, subscribe to our mailing list. There, you will find content such as:
- Our blog posts
- References to papers we publish with other clients or research institutions
- Reference to events in which we will participate/sponsor
- An aggregate of content we recommend (e.g. papers, libraries, books, opinion articles, softwares, online courses, …)