#!/usr/bin/env python
# coding: utf-8

# ### Introduction
# 
# After we have created a model we will typically want to save it as a binary file so it can be deployed for prediction.
# 
# Seldon core is a model deployment engine.  Here is an excerpt from the seldon documentation for Scikit-learn deployments
# 
# > Seldon expects that your model has been saved using joblib, and it named as model.joblib. Note that this is the recommended approach to serialise models by the SKLearn project.
# >
# > _Source: https://docs.seldon.io/projects/seldon-core/en/latest/servers/sklearn.html_

# ### Saving a model
# 
# Here we use the [joblib](https://joblib.readthedocs.io/en/latest/persistence.html) Python library to save a model:

# In[8]:


# set the random state to make the examples repeatable
import numpy as np
np.random.seed(1)

from sklearn.datasets import load_iris
from sklearn.svm import LinearSVC

iris = load_iris()
X, y = iris.data, iris.target

classifier = LinearSVC()
model = classifier.fit(X, y)

from joblib import dump, load
dump(model, 'iris_model.joblib') 


# We can execute a shell command with `!`. Let's inspect the saved file:

# In[9]:


get_ipython().system(' ls -l iris_model.joblib')


# ### Summary
# 
# More information on model persistence can be found in the Scikit-learn [docs](https://scikit-learn.org/stable/modules/model_persistence.html#)

# ---
# 
# ### Navigation
# 
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