Each Aim is has one serving API, which you can use to get predictions. For this aim, the API is

https://....../Day2Example?features=[...]
https://....../Day2Example?batch=[...]

The API will return the predictions of all deployed models:

[{ model1: "yes", model2: "no", ...}...]

Deployed models are a collection of models that are used for each call to the serving API.

All deployed models are stored in the branch deployed_models.

All Deployed Models are choosen from a set of deployment candidates, which are stored in the branch deployment_candidates.

A user can manually choose to commit deployment candidates to deployed models.

A user can call Model picker, which takes as input the current deployment candidates and deployed models, and produce a new set of deployed models. This creates a new commit in the branch deployed_models.

Each Trial is identified by one unique training set uploaded by the user. Each trail has its own branch. Each commit in the trial branch contains:

• training set: The state of the training set
• search space: The search space of AutoML
• models: The results of all models
• other meta data, e.g., labeling functions

Every call to "What's Next" will create a set of suggested transformations. Each transformation takes as input one commit in a trail and bring it to a new commit:

• new training set: after data cleaning, labeling etc.
• new search space
• new models
• new meta data

The user might choose to move some models in each trial to the deployment candidate branch. This creates a new commit in the deployment_candidates branch and is where CI/CD gets triggered.

All trials in an Aim shares the same validation dataset, which is stored in its own branch validation_set.

Each call to the serving APIs will be logged and use as the fresh production set. By defaulted they are not logged in git.