Update how-to-deploy-mlflow-models.md

Author: santiagxf

articles/machine-learning/how-to-deploy-mlflow-models.md

@@ -239,7 +239,7 @@ For those cases, you can either [change how your model is being logged in the tr
 
 When you log a model using either `mlflow.autolog` or using `mlflow.<flavor>.log_model`, the flavor used for the model decides how inference should be executed and what gets returned by the model. MLflow doesn't enforce any specific behavior in how the `predict()` function generates results. However, there are scenarios where you probably want to do some pre-processing or post-processing before and after your model is executed. On another scenarios, you may want to change what's returned like probabilities vs classes.
 
-A solution to this scenario is to implement machine learning pipelines that moves from inputs to outputs directly. For instane, [`sklearn.pipeline.Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) is a very popular (and sometimes encourageable for performance considerations) way to do so. Another alternative is to [customize how your model does inference using a custom model flavor](how-to-log-mlflow-models.md?#logging-custom-models).
+A solution to this scenario is to implement machine learning pipelines that moves from inputs to outputs directly. For instance, [`sklearn.pipeline.Pipeline`](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) or [`pyspark.ml.Pipeline`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.Pipeline.html) are very popular (and sometimes encourageable for performance considerations) ways to do so. Another alternative is to [customize how your model does inference using a custom model flavor](how-to-log-mlflow-models.md?#logging-custom-models).
 
 ### Customize inference with a scoring script