BUILD GIT ANNEX SOURCE UPDATE

A reference url to the training dataset is stored in a.

if the engineer forgets to increment the VERSION file).

Attempting to upload the same version of the model will cause the upload to fail (i.e.

This package can only ever contain one version of the model to prevent confusion.

Data preparation and feature engineering steps underpinning the training are captured through a scikit-learn pipeline.

Every time a feature branch is merged into master, a new version of the model is trained and published to a package index (Gemfury) in CI.

Prior to using DVC, the way this system aimed for reproducibility was: The training data is 2GB and is not kept under version control. The model in question is a Convolutional Neural Network for image classification, which uses data from the Kaggle v2 Plant Seedlings Dataset. This project has a reasonably realistic setup for a production machine learning model deployment, complete with CI/CD pipeline integration and model versioning.

BUILD GIT ANNEX SOURCE UPDATE

I chose to update the example project from the Train In Data course: Deployment of Machine Learning Models. In order to test these capabilities, I decided to introduce DVC into one of my ML projects. Versioning and experimentation management on top of git.

Lightweight pipelines with reproducibility built in.

In terms of core features, DVC is centered around: One of the biggest challenges in reusing, and hence the managing of ML projects, is its reproducibility DVC has been built to address the reproducibility. DVC enters this mix offering a cleaner solution, specifically targeting Data Science challenges. In 2019, we tend to find organizations using a mix of git, Makefiles, ad hoc scripts and reference files to try and achieve reproducibility. The thing is, there is a lot involved in the above steps.

Dependency management (including of your data and infrastructure).

Capturing the exact steps in your data munging and feature engineering pipelines.

can you recreate a prediction you have made in the past? HereвЂ™s a talk Soledad Galli and I gave on this topic - paramount among reproducibility concerns are the following: Chief among these challenges is the need for reproducibility, i.e. The technical debt of ML systems has been best documented by Sculley et al. Once machine learning models leave the research environment, and particularly when they need to be regularly updated in production, a host of challenges start to become apparent. Machine Learning System Challenges and DVC What follows are my initial thoughts after giving the system a test for a day. I was recently approached by the team developing Data Science Version Control (DVC).

Build git annex source