The major pitfall - it depends on your goal. Your approach looks good if you just need to retrain an existing model\code in production. However, this approach i

The major pitfall - it depends on your goal. Your approach looks good if you just need to retrain an existing model\code in production. However, this approach is not perfect in the ML modeling\development stage. Let me explain... `--entrypoint` defines a single step\script. You make a single black box from your entire ML processes. There is a lack of granularity for ML modeling process when people tend to separate different stages to make the process more manageable and efficient: manage and version dataset separately from modeling, preprocess data before training, training code as a separate unit plus some problem specific steps\units. DVC gives you the ability to increase the granularity of your ML project while still keeps it manageable and reproducible. The steps still can be wrapped in Docker - it is a good practice. As @theossuary said, run `docker run -ti experiments:latest` as a step in DVC.