IvanNardini

model_name = 'Champion'
artifact_path = "model"
model_uri = "runs:/{run_id}/{artifact_path}".format(run_id=run_id, artifact_path=artifact_path)
model_details = mlflow.register_model(model_uri=model_uri, name=model_name)

client = MlflowClient()

client.update_registered_model(
  name=model_details.name,
  description="This model provides recommendation for specific users and items based on purchase data. The data consists of user transactions"

IvanNardini

def reader(df):
    """
    return a data parsed with Reader class
    args:
        df: pandas dataframe
    returns:
        data parsed
    """
    mindata = df.rating.min()
    maxdata = df.rating.max()

IvanNardini

└── src
    └── score_interactive_endpoint
        ├── Dockerfile
        ├── app.py
        ├── callbacks.py
        ├── layouts.py
        ├── model
        │   └── model_aa0lcc9j.pkl
        ├── requirements.txt
        └── run.py

IvanNardini

'''
This module contains the predict callback 
'''
from dash.dependencies import Input, Output

from app import *

@app.callback([Output('table', component_property='columns'), Output('table', component_property='data')],[Input(component_id='uid', component_property='value')])
def predict(uid):
    columns = []

IvanNardini

'''
This module contains the layout of the application
'''

import dash_table
import dash_core_components as component
import dash_html_components as html

#Customerid Input
c_id_component = component.Input(id="uid", 

IvanNardini

'''
This module runs the entire Dash Application
'''

import dash_table
import dash_core_components as component
import dash_html_components as html
from dash.dependencies import Input, Output

from app import *

IvanNardini

# Define tracking function
def log_lineareg(experimentID, run_name, params, abt_train, abt_test, debug=False):
  
  """
  Function to start a run within a existing experiment
  :param experimentID: unique ID associated to original experiment
  :param run_name: label for the name of the run
  :param params: ters used for the run, such as arguments
  :param abt_train: analytical base table for training 
  :param abt_test: analytical base table for testing

IvanNardini

%sh 
rm -rf /tmp/mleap_python_model_export
mkdir /tmp/mleap_python_model_export
ls -la /tmp/mleap_python_model_export
#Serialize Model to Bundle
lrModel.serializeToBundle("jar:file:/tmp/mleap_python_model_export/lrModel.zip", predictions)
%sh 
ls -la /tmp/mleap_python_model_export/
dbutils.fs.cp("file:/tmp/mleap_python_model_export/lrModel.zip", "dbfs:/example/lrModel.zip")
display(dbutils.fs.ls("dbfs:/example"))

IvanNardini

#!/bin/bash

# setup.sh
# Create a bucket if doesn't exist. 
# And load deployment scripts(.sh, .py)
# REGION  - Region name (default eu)
# BUCKET - Bucker name (default cloud-demo-databrick-gcp)


#Pass REGION and BUCKET names (or use default parameters)

IvanNardini

#!/bin/bash

# setup_cluster.sh
# Create a plain vanilla cluster if doesn't exist with config.
# REGION  - Region name (default eu)
# BUCKET - Bucker name (default cloud-demo-databrick-gcp)


#Pass all parameters (or use default parameters)
CLUSTER_NAME=${1:-cluster-00000}