How can I train a model using a combination of Feature Store features and external data, and then perform batch inference using MLflow while ensuring that the necessary columns are included in the DataFrame at inference time?
To train a model using a combination of Feature Store features and external data, you can follow these steps:

1. Define the feature lookups for the features you want to use from the Feature Store. For example:
```python
feature_lookups = [
    FeatureLookup(
        table_name='recommender_system.customer_features',
        feature_names=['total_purchases_30d'],
        lookup_key='customer_id',
    ),
]
```
2. Create a DataFrame that includes both the external data and the primary key(s) used in the feature lookups. For example:
```python
# df has columns ['customer_id', 'browser', 'rating']
df =...
```
3. Use the `create_training_set` method of the `FeatureEngineeringClient` (for Feature Engineering in Unity Catalog) or `FeatureStoreClient` (for Workspace Feature Store) to create a training set that includes the feature lookups and the label. For example:
```python
fe = FeatureEngineeringClient()
training_set = fe.create_training_set(
    df=df,
    feature_lookups=feature_lookups,
    label='rating',
    exclude_columns=['customer_id'] # 'browser' is not excluded
)
```
4. Train the model using the training set. For example:
```python
model =...
model.fit(training_set)
```
5. Log the model using the `log_model` method of the `FeatureEngineeringClient` or `FeatureStoreClient`. For example:
```python
fe.log_model(model_uri=model_uri, model=model)
```

To perform batch inference using MLflow while ensuring that the necessary columns are included in the DataFrame at inference time, you can follow these steps:

1. Load the model using MLflow. For example:
```python
import mlflow.pyfunc

model_uri =...
model = mlflow.pyfunc.load_model(model_uri)
```
2. Create a DataFrame that includes the primary key(s) used in the feature lookups and any other columns that are required at inference time. For example:
```python
# batch_df has columns ['customer_id', 'browser']
batch_df =...
```
3. Use the `predict` method of the loaded model to perform batch inference. For example:
```python
predictions = model.predict(batch_df)
```

Note that the `predict` method will automatically retrieve the feature values from the Feature Store and join them with any values provided in the DataFrame at inference time. However, you must ensure that the DataFrame includes the primary key(s) used in the feature lookups.