Usage¶

After defining the configuration file with the necessary credential and model details, consider the following scenario.

A data scientist is working on 5 models,

German Credit Risk-SGD - Stochastic gradient descent classifer
German Credit Risk-RF - Random forest classifer
German Credit Risk-SVC - Support vector classifer
German Credit Risk-custom - Custom ML model hosted externally
German Credit Risk-GBC - Gradient boosting classifer

Assume the following tasks,

Store German Credit Risk-SGD in the project space
Deploy German Credit Risk-RF to the development space
Validate German Credit Risk-SVC and German Credit Risk-custom in the development enviroment
Operate German Credit Risk-GBC in the production enviroment

The above tasks can be easily achieved by either the CLI or Python API using the below commands,

Using the CLI¶

# store model in project space
cpdflow apply develop -c config.json -m "German Credit Risk-SGD"
`  `

# deploy model in development space
cpdflow apply deploy -c config.json -m "German Credit Risk-RF" -s "dev"

# validate model in development space
cpdflow apply validate -c config.json -m "German Credit Risk-SVC" -m "German Credit Risk-custom"

# operate model in production space
cpdflow apply operate -c config.json -m "German Credit Risk-GBC"

Using the Python API¶

import cpdflow

config = {} # define the configuration file, refer to the configuration file documentation

# store model in project space
cpdflow.apply.develop(config=config, model_names=["German Credit Risk-SGD"])

# deploy model in development space
cpdflow.apply.deploy(config=config, model_names=["German Credit Risk-RF"], space_type="dev")

# validate model in development space
cpdflow.apply.validate(config=config, model_names=["German Credit Risk-SVC", "German Credit Risk-custom"])

# operate model in production space
cpdflow.apply.operate(config=config, model_names=["German Credit Risk-GBC"])

Below is the the final output in Model Inventory.