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• date: 22 March 2025
• posted by: AD-TEAM

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

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




   
   
   

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