Deploying ML Models in Production | CodeSignal Learn

intermediate

Deploying ML Models in Production

Machine Learning

3 courses

58 practices

7 hours

Learn to deploy ML models in production! In this path, you'll learn how to build reusable pipeline functions, create FastAPI web services, and automate retraining with Apache Airflow. Get ready to transform your ML code from individual scripts to scalable, production-ready systems.

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Earn a shareable

Certificate of Achievement

Verified skills you'll gain

DEVELOPING

Machine Learning Model Development

INTERMEDIATE

Model Deployment Processes

Tools you'll use

Apache Airflow

FastAPI

Pandas

pytest

Python

Scikit-learn

Trusted by learners working at top companies

Course 1

Building Reusable Pipeline Functions

4 lessons

21 practices

This course lays the groundwork for a robust MLOps pipeline by developing core functions that will be reused in subsequent courses. Rather than focusing on the full data science process, learners will implement specific, modular components for data processing, model training, evaluation, and persistence—all critical for later integration in automated retraining and API-based serving.

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Course 2

Model Serving with FastAPI

3 lessons

17 practices

In this course, learners transition to model serving by integrating their ML model into a web service using FastAPI. The focus is on creating a functional API that leverages the model persistence function from Course 1 and ensures that the prediction endpoint is both robust and secure.

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Course 3

Automating Retraining with Apache Airflow

4 lessons

20 practices

This course introduces the orchestration of an automated retraining pipeline using Apache Airflow. Learners will design a workflow that integrates data processing, model training, and evaluation, ensuring that the ML model stays up-to-date. The course emphasizes real-world scheduling, error handling, and optimization of the automated tasks.

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