Deploying Python Applications on Google Cloud Platform

Deploying Python Applications on Google Cloud Platform | Udemy [Update 03/2025]
English | Size: 947 MB
Genre: eLearning

From Training to Cloud: Deploying Machine Learning Models on GCP with Python

What you’ll learn
Explore key platform services like Google Compute Engine (GCE), App Engine (GAE), Kubernetes Engine (GKE), Cloud Run, and Cloud Functions
Determine the most suitable service for each type of application
Train and evaluate a CNN model, including creating a Python project locally that’s ready for deployment
Deploy your machine learning application across multiple GCP services, learning to configure environments and manage resources
Prevent unnecessary costs by properly cleaning up resources after deployment

Learning to implement machine learning models in production is a critical skill for data scientists who want to move beyond theoretical analysis and create practical business impact. While building models is essential, it is during deployment that these solutions come to life, becoming accessible to end users and integrating into real-world systems. Mastering this phase allows data scientists to ensure the scalability of their solutions, monitor performance in dynamic environments, and collaborate effectively with development and operations teams. Additionally, understanding the full lifecycle—from training to cloud deployment—enhances professional relevance, positioning data scientists as strategic players capable of delivering tangible value from conception to operation.

This introductory course is designed for developers, machine learning enthusiasts, and data professionals who want to learn how to deploy their first AI applications on the web using Google Cloud Platform (GCP). Through a hands-on approach, you will be guided from training a convolutional neural network (CNN) for image classification to deploying the model on scalable cloud services. The course includes an introduction to key GCP services such as Google Compute Engine (GCE), App Engine (GAE), Kubernetes Engine (GKE), Cloud Run, and Cloud Functions, enabling you to compare and choose the best option for your project.

In the first stage, you will set up your local environment: import libraries (like TensorFlow/Keras), train and evaluate your CNN model, and create a simple Python application to integrate with the trained model. Next, you will learn how to configure GCP and deploy to different services.

Ideal for cloud computing beginners and professionals looking to put machine learning models into production. By the end, you will have deployed a functional web application for image classification in the cloud, mastering the full development cycle—from model training to deployment on Google’s professional services.

Who this course is for:
Cloud computing beginners looking to take their first steps with GCP
Data scientists and Python developers aiming to deploy machine learning models in production