deploy ml

convert.py

import tensorflow as tf

# create and train a keras neural network
classifier = tf.keras.models.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1]),
    tf.keras.layers.Dense(units=28, activation='relu'),
    tf.keras.layers.Dense(units=1)
])
classifier.compile(optimizer='sgd', loss='mean_squared_error')
classifier.fit(x=[-1, 0, 1], y=[-3, -1, 1], epochs=5)


# Convert the model to a Tensorflow Lite object
converter = tf.lite.TFLiteConverter.from_keras_model(classifier)
tfl_classifier = converter.convert()

# Save the model as a .tflite file
with open('classifier.tflite', 'wb') as f:
  f.write(tfl_classifier)

load.py

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

df = pd.read_csv('titanic.csv')

x = df[df.columns.difference(['Survived'])
y = df['Survived']
       
classifier = RandomForestClassifier()
classifier.fit(x, y)

persist.py

from sklearn.externals import joblib

joblib.dump(classifier, 'classifier.pkl')

schedule.py

import requests
from datetime import timedelta, datetime

import pandas as pd

from prefect import task, Flow
from prefect.schedules import IntervalSchedule

@task(max_retries=3, retry_delay=timedelta(5))
def predict(input_data_path:str):
    """
    This task load the saved model, input data and returns prediction.
    If failed this task will retry 3 times at 5 min interval and fail permenantly.
    """
    
    classifier = joblib.load('classifier.pkl')
  
    df = pd.read_csv(input_data_path)
  
    prediction = classifier.predict(df)
  
    return jsonify({'prediction': list(prediction)})
  
  
@task(max_retries=3, retry_delay=timedelta(5))
def save_prediction(data, output_data_path:str):
    """
    This task will save the prediction to an output file. 
    If failed, this task will retry for 3 times and fail permenantly.
    """
    with open(output_data_path, 'w') as f:
      f.write(data)
    

# Create a schedule object. 
# This object starts 5 seconds from the time of script execution and repeat once a week. 
schedule = IntervalSchedule(
    start_date=datetime.utcnow() + timedelta(seconds=5),
    interval=timedelta(weeks=1),
)

# Attach the schedule object and orchastrate the workflow.
with Flow("predictions", schedule=schedule) as flow:
    prediction = predict("./input_data.csv")
    save_prediction(prediction. "./output_data.csv")
    

serve.py

from flask import Flask

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
     json_ = request.json
     query_df = pd.DataFrame(json_)
     query = pd.get_dummies(query_df)
    
     classifier = joblib.load('classifier.pkl')
     prediction = classifier.predict(query)
     return jsonify({'prediction': list(prediction)})


if __name__ == '__main__':
     app.run(port=8080)

Hi, thunk for posting this example, I think here is some err brackets not closing
https://gist.github.com/thuwarakeshm/c47d05526c545d329700e649f9ff2384/1d18360f32e4e28a14a82c811ad7e33edb0c7cd0#file-load-py