Tooling in Maching Learning Lifecycle
Link to full Talk
This talk was on the importance of tooling in a ML development lifecyle.
- Importance of tracking the various metrics involved in training the model - training parameters, training results
- Tieing up the model with the datasets and the training params
- Packaging the model in a standard format to be later deployed to any modern tech stack.