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

Requirements

Basic knowledge of Python and machine learning (prior experience with neural networks is a plus)
Familiarity with web development concepts (optional but recommended)

Description

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.

Overview

Section 1: Introduction
Lecture 1 Course content
Lecture 2 Course materials
Lecture 3 Technical terms
Lecture 4 Google Cloud Platform services 1
Lecture 5 Google Cloud Platform services 2
Section 2: Preparing the application
Lecture 6 Importing the libraries
Lecture 7 Loading the dataset
Lecture 8 Creating and training the model
Lecture 9 Model evaluation
Lecture 10 Creating a local project
Lecture 11 Creating a Python app 1
Lecture 12 Creating a Python app 2
Section 3: Deploying Python app on GCP
Lecture 13 Preparing Google Cloud Platform
Lecture 14 Deploy on Google Compute Engine (GCE) 1
Lecture 15 Deploy on Google Compute Engine (GCE) 2
Lecture 16 Deploy on Google App Engine (GAE)
Lecture 17 Deploy on Google Kubernetes Engine (GKE)
Lecture 18 Deploy on Cloud Run
Lecture 19 Deploy on Cloud Run Functions
Lecture 20 Avoid charges: cleaning the environment
Section 4: Final remarks
Lecture 21 Final remarks
Lecture 22 BONUS
Cloud computing beginners looking to take their first steps with GCP,Data scientists and Python developers aiming to deploy machine learning models in production

Homepage:

https://www.udemy.com/course/deploying-python-applications-on-google-cloud-platform/