James Le wrote this article in which he wants to show 3 powerful tools to simplify and scale up machine learning development within an organization by making it easy to track, reproduce, manage, and deploy models.
Managing a machine learning workflow is hard. Beyond the usual challenges in software engineering, machine learning engineers also need to think about experiment tracking, reproducibility, model deployment, and governance.
The article discusses the following:
- Using version control with DVC
- Tracking experiments with Weights & Biases
- Deploying Models with Docker
DVC is a handy tool built to make machine learning models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Using emerging platforms can keep your projects organized and make you more productive as a machine learning engineer. Nice one!
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