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Creating and Serving your First Machine Learning Model

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Welcome to the world of machine learning, where computers learn from data and make predictions without explicit programming. At the heart of this technology lies the concept of a “model.”

What is a Model

In traditional programming, we create functions/methods that receive inputs/parameters and return a result based on a formula. For example, imagine a Java method that applies the formula y = 3x + 1.

public int formula(int x) {
    return 3 * x + 1;

The above code would return the following data for x and y:

x -1 0 1 2 3 4
y -2 1 4 7 10 13

Now, imagine that rather than the formula, you have lots of x and y values. You can create a machine learning model to discover the formula and predict new values.

As a real-life example, we can use the facial recognition that happens in the gallery of our phones. We have several inputs (photos) and outputs (people’s names) and the machine learning model is the formula that knows how to recognize people. As you give names to people in the photos, you’re feeding the model with data that is constantly retrained to better recognize those people.

Python: The Language of Machine Learning

Python has become the de facto language for machine learning. Its vast ecosystem of libraries, including TensorFlow and Keras, makes it a powerhouse for building and training models. If you’re curious about stepping into the world of machine learning, Python is your trusty companion on this journey.

Our Model

For simplicity, we’ll use the x and y data above to train a model that will know how to predict a y value based on x.

import tensorflow as tf
import numpy as np
from tensorflow import keras
import os

def build_model():
    # Create a model that receives 1 input value and returns 1 output value
    model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])

    # Define the algorithms for learning. You don't need to worry about this for now
    model.compile(optimizer='sgd', loss='mean_squared_error')
    return model

def train_model(model, xs, ys, epochs=500):
    # Train the model. Here we're saying the algorithm to try 500 random formulas to find the one that best matches
    # the input and output data., ys, epochs=epochs)

def predict_with_model(model, input_data):
    # Predict using the trained model
    return model.predict([input_data])

def save_model(model, export_path):
    # Save the model

def main():
    # Input data
    xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)
    ys = np.array([-2.0, 1.0, 4.0, 7.0, 10.0, 13.0], dtype=float)

    # Build the model
    model = build_model()

    # Train the model
    train_model(model, xs, ys)

    # Predict the value for x = 10. It will print a number very close to 31, like 30.9994 or something
    prediction = predict_with_model(model, 10.0)

    # Save the model
    model_dir = "./model"
    version = 1
    export_path = os.path.join(model_dir, str(version))
    print('export_path = {}\n'.format(export_path))
    save_model(model, export_path)
    print('\nSaved model: ' + export_path)

if __name__ == "__main__":

Run the above Python code to create, train, and test the model. It will create the model under the ./model directory.

Serving the model

Once you created the model and have it under the ./model directory, you can serve it as a REST API. To do so, you can use the tensorflow/serving container image:

podman run -p 8501:8501 \
    --name=tf_serving \
    --mount type=bind,source=./model,target=/models/model -e MODEL_NAME=model \
    -t tensorflow/serving

Consuming the model

Once your container is up and running, you can send a request to make an inference. Run the following command to infer the y value for x = 10:

curl -d '{"instances": [[10.0]]}' \
    -H "Content-Type: application/json" \
    -X POST http://localhost:8501/v1/models/model:predict

You should see a result similar to the following:

    "predictions": [[30.9971237]

That’s all Folks!

You’ve just created, trained, served, and consumed your first machine learning model. You can find the source code used in this post on GitHub. Feel free to ask any questions in the comments and stay tuned for more.