Deploying an MLflow model as a CML Model REST API

In the future, you will be able to register models to a Model Registry and then deploy Model REST APIs with those models. Today, these models can be deployed using the following manual process instead

Navigate to your project. Note that models are always created within the context of a project.
Click Open Workbench and launch a new Python 3 session.
Create a new file within the project if one does not already exist: cdsw-build.sh This file defines the function that will be called when the model is run and will contain the MLflow prediction information.
Add the following information to the cdsw-build.sh file: pip3 install sklearn mlflow pandas
For non-Python template projects and old projects check the following.
1. Check to make sure you have a .gitignore file. If you do not have the file, add it.
2. Add the following information to the .gitignore file: !.experiments
For new projects using a Python template, this is already present.
Create a Python file to call your model artifact using a Python function. For example:
- Filename: mlpredict.py
- Function: predict
Copy the MLflow model file path from the Make Predictions pane in the Artifacts section of the Experiments/Run details page and load it in the Python file. This creates a Python function which accepts a dictionary of the input variables and converts these to a Pandas data frame, and returns the model prediction. For example:
```
import mlflow
import pandas as pd
logged_model =
     '/home/cdsw/.experiments/7qwz-l620-d7v6-1922/glma-oqxb-szc7-c8hf/artifacts/model'
def predict(args):
     # Load model as a PyFuncModel.
     data = args.get('input')
     loaded_model = mlflow.pyfunc.load_model(logged_model)
     # Predict on a Pandas DataFrame.
     return loaded_model.predict(pd.DataFrame(data))
```
note
In practice, do not assume that users calling the model will provide input in the correct format or enter good values. Always perform input validation.
Deploy the predict function to a REST endpoint.
1. Go to the project Overview page
2. Click Models > New Model.
3. Give the model a Name and Description
4. Enter details about the model that you want to build. In this case:
  - File: mlpredict.py
  - Function: predict
  - Example Input:
```
{
"input": [
[7.4, 0.7, 0, 1.9, 0.076, 11, 34, 0.9978,
3.51, 0.56, 9.4]
]
}
```
  - Example output:
```
[
5.575822297312952
]
```
5. Select the resources needed to run this model, including any replicas for load balancing.
  note
  The list of options here is specific to the default engine you have specified in your Project Settings: ML Runtimes or Legacy Engines. Engines allow kernel selection, while ML Runtimes allow Editor, Kernel, Variant, and Version selection. Resource Profile list is applicable for both ML Runtimes and Legacy Engines.
6. Click Deploy Model.
Click on the model to go to its Overview page.
Click Builds to track realtime progress as the model is built and deployed. This process essentially creates a Docker container where the model will live and serve requests.
Once the model has been deployed, go back to the model Overview page and use the Test Model widget to make sure the model works as expected. If you entered example input when creating the model, the Input field will be pre-populated with those values.
Click Test. The result returned includes the output response from the model, as well as the ID of the replica that served the request.
Model response times depend largely on your model code. That is, how long it takes the model function to perform the computation needed to return a prediction. It is worth noting that model replicas can only process one request at a time. Concurrent requests will be queued until the model can process them.