Deploy Machine Learning Models & Build AI Agents using n8n

Machine Learning models are often taught in theory, but many learners struggle to understand how these models are actually used in real-world applications. While training a model is important, the real value of Machine Learning comes from deploying it, integrating it with other systems, and using it as part of an automated workflow. This course is designed to bridge that gap by showing you how to take a trained Machine Learning model and turn it into a working, automated solution.

In this hands-on course, you will start by building a simple Machine Learning model using Python and popular libraries such as scikit-learn. Instead of focusing on complex mathematics, the emphasis is on understanding the workflow and practical usage of ML models. Once the model is trained, you will learn how to deploy it as an API using Flask, allowing external systems to send data and receive predictions.

Next, you will deploy this ML API to the cloud using Render, making your model accessible from anywhere. This step helps you understand how real-world ML systems are exposed and used outside local environments. After deployment, you will integrate the live ML API with n8n, a powerful automation tool, to build an automated AI agent. This AI agent will be capable of triggering predictions as part of a workflow, demonstrating how Machine Learning can be combined with automation to solve real problems.

Instead of heavy theory, this course focuses on practical implementation and real-world understanding. By the end of the course, you will clearly understand how Machine Learning models, APIs, cloud deployment, and automation tools like n8n work together in production-style systems. This course serves as a strong foundation for learners who want to move toward AI agents, ML automation, and MLOps concepts in the future.