Deploying Locally Trained Models to SageMaker

Introduction & Lesson Overview

Welcome to your first lesson on deploying models with SageMaker! You've already built a solid foundation by training machine learning models both locally and in the cloud using SageMaker. Now it's time to take the next crucial step in your machine learning journey: making your models available for real-world predictions through deployment. In this lesson, you'll learn how to deploy a model that you trained on your local machine to a SageMaker endpoint. This is an excellent starting point because you can use the familiar models you've already created while learning the essential deployment concepts that apply to all SageMaker deployments. By the end of this lesson, you'll understand how to package your local model artifacts, upload them to Amazon S3, configure a SageMaker deployment environment, and create a working endpoint that can serve predictions. This foundational knowledge will prepare you for more advanced deployment scenarios later in the course.

Understanding SageMaker Endpoints

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Deploying Your Model to a SageMaker Serverless Endpoint

With your model configured, you're ready to deploy it to a live SageMaker endpoint. The deployment process creates the necessary AWS infrastructure and makes your model available for real-time predictions:

Python

# Deploy model as serverless endpoint asynchronously
predictor = model.deploy(
    serverless_inference_config=serverless_config,
    endpoint_name=ENDPOINT_NAME,
    wait=False
)

# Retrieve detailed information about the specified SageMaker endpoint
endpoint_description = sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=ENDPOINT_NAME)

# Extract the current status of the endpoint from the response
status = endpoint_description['EndpointStatus']

# Display the endpoint's status
print(f"Endpoint status: {status}")

# Deploy model as serverless endpoint asynchronously
predictor = model.deploy(
    serverless_inference_config=serverless_config,
    endpoint_name=ENDPOINT_NAME,
    wait=False
)

# Retrieve detailed information about the specified SageMaker endpoint
endpoint_description = sagemaker_session.sagemaker_client.describe_endpoint(EndpointName=ENDPOINT_NAME)

# Extract the current status of the endpoint from the response
status = endpoint_description['EndpointStatus']

# Display the endpoint's status
print(f"Endpoint status: {status}")

The model.deploy() method creates your live endpoint infrastructure. We set wait=False to deploy asynchronously, which means the method returns immediately instead of blocking your code for several minutes while AWS provisions the resources.

Since the deployment happens in the background, we need to actively check its progress. The describe_endpoint() call shows us the current deployment status. Initially, you'll see:

text

Endpoint status: Creating

Endpoint status: Creating

Your endpoint moves through a predictable lifecycle: it starts as Creating while AWS sets up the infrastructure, then transitions to InService when ready for predictions, or Failed if something goes wrong. The deployment typically takes 2-5 minutes for serverless endpoints. If you prefer to wait automatically, you can set wait=True in the deploy() method and your code will block until the endpoint is fully deployed. Alternatively, you can periodically run the describe_endpoint() code above to manually check the status. Once you see InService, your model is live and ready to handle requests. Until then, you'll need to wait for the provisioning process to complete.

Configuring Data Serialization

While the Predictor handles the HTTP communication, your Python objects (like pandas DataFrames and numpy arrays) can't travel over the internet in their current form. They exist only in your local memory, but they need to be converted into a format that can be transmitted to AWS servers and understood by your deployed model.

Python

from sagemaker.serializers import CSVSerializer
from sagemaker.deserializers import CSVDeserializer

# Set the serializer to convert input data (numpy arrays, pandas DataFrames) to CSV format for the endpoint
predictor.serializer = CSVSerializer()

# Set the deserializer to convert the CSV response from the endpoint back to Python objects (e.g., numpy arrays)
predictor.deserializer = CSVDeserializer()

from sagemaker.serializers import CSVSerializer
from sagemaker.deserializers import CSVDeserializer

# Set the serializer to convert input data (numpy arrays, pandas DataFrames) to CSV format for the endpoint
predictor.serializer = CSVSerializer()

# Set the deserializer to convert the CSV response from the endpoint back to Python objects (e.g., numpy arrays)
predictor.deserializer = CSVDeserializer()

This is where serialization becomes crucial. Think of it like sending a photo via text message: you can't just "send the photo" - your phone converts the image into text characters, transmits those characters, and the recipient's phone converts them back into a viewable image.

The same process happens here: when you call predictor.predict(), the CSVSerializer automatically converts your pandas DataFrame into CSV text format that fits inside an HTTP request body. Your endpoint receives this CSV text, feeds it to your model, and sends back predictions as CSV text in the HTTP response. The CSVDeserializer then converts that response back into Python arrays you can immediately use for analysis.

We chose CSV format because it's perfect for tabular data like our housing features - it's simple, widely supported, and easily readable by both humans and machines. With these serializers configured, your Predictor now has everything it needs to seamlessly bridge the gap between your local Python environment and your cloud-hosted model.

Evaluating Model Performance

With our predictor properly configured to handle the data conversion and HTTP communication, we can now send real data to test the deployed model's performance. This evaluation serves a critical purpose: verifying that your model works correctly in the production environment and maintains the same accuracy you observed during local development.

Python

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score

# Path to local test dataset
TEST_DATA_FILE = "data/california_housing_test.csv"

# Load and split test data into features and target variable
test_df = pd.read_csv(TEST_DATA_FILE)
X_test = test_df.drop("MedHouseVal", axis=1)
y_test = test_df["MedHouseVal"]

# Make predictions on entire test set
predictions = predictor.predict(X_test.values)

# Evaluate the model
test_r2 = r2_score(y_test, predictions)
test_rmse = np.sqrt(mean_squared_error(y_test, predictions))

# Print the results
print(f"R² Score: {test_r2:.4f}")
print(f"RMSE: {test_rmse:.4f}")

import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score

# Path to local test dataset
TEST_DATA_FILE = "data/california_housing_test.csv"

# Load and split test data into features and target variable
test_df = pd.read_csv(TEST_DATA_FILE)
X_test = test_df.drop("MedHouseVal", axis=1)
y_test = test_df["MedHouseVal"]

# Make predictions on entire test set
predictions = predictor.predict(X_test.values)

# Evaluate the model
test_r2 = r2_score(y_test, predictions)
test_rmse = np.sqrt(mean_squared_error(y_test, predictions))

# Print the results
print(f"R² Score: {test_r2:.4f}")
print(f"RMSE: {test_rmse:.4f}")

We begin by loading our test dataset and separating the input features from the target values we want to predict. The crucial step happens with predictor.predict(X_test.values) - this call triggers the entire serialization process we just described, converting your data to CSV, sending it to the SageMaker endpoint, and receiving the model's predictions back.

The evaluation metrics provide the final validation of your successful deployment. Typical results might look like:

text

R² Score: 0.6456
RMSE: 0.6814

R² Score: 0.6456
RMSE: 0.6814

When these metrics align with your local results, you can be confident that your deployment process preserved your model's predictive capabilities and that your endpoint is ready for real-world use.

Introduction & Lesson Overview

Understanding SageMaker Endpoints

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Packaging Your Local Model for Deployment

Uploading Model Artifacts to Amazon S3

Setting Up Configuration Variables

Creating the Entry Point Script

Creating the SageMaker Model Object

Configuring Serverless Inference Settings

Deploying Your Model to a SageMaker Serverless Endpoint

Connecting to Your SageMaker Endpoint

Configuring Data Serialization

Evaluating Model Performance

Summary & Next Steps