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Deploy a LLM Application to Vercel, Google Cloud & Azure

Name: Deploy a LLM Application to Vercel, Google Cloud & Azure
Rating: 4.6 (515 reviews)

Hands-on guide to building a Python-based Generative AI LLM app and deploying it on Vercel, Google Cloud Run, and Azure

Created byAnkit Mistry : 266,000+ Students, Data Science & Machine Learning Academy

Last updated 2/2026

English

English [Auto],Japanese [Auto],

What you'll learn

Build and deploy a Generative AI web application
Confidently use Docker for Python applications
Deploy applications on Vercel, Google Cloud, and Azure
Understand real-world cloud deployment workflows
Add a multi-cloud GenAI project to your portfolio
Speak confidently about deployment in interviews

Course content

16 sections • 75 lectures • 6h 13m total length

1. Create vercel Account - Signup4:34
2. Install IDE in Local Machine - VS Code3:27
3. Python Installation, uv and Project Setup4:27
4. Create Hello World Web Application6:58
Build a simple hello world web app with FastAPI or Flask and uvicorn. Set up a virtual environment and requirements.txt, run locally, then preview Vercel deployment.
5. Deploy Application to vercel8:36
deploy your application to the vercel cloud by installing the vercel cli with npm, configuring vercel.json, logging in, and deploying to obtain a live link for validation.
6. Create Flash Card Gen AI Application Structure7:01
restructure the project into a flashcard gen ai app with a new virtual environment and pyproject.toml, preparing main.py, openai client, and templates for chat gpt api.
1.7 Create Open AI API Key2:11
Acquire an OpenAI API key via the JetGPT platform or Google Gemini, log in, create a secret key with a $5 credit, and prepare for API-based flashcard generation.
1.8 Flashcard App Part - 26:39
Build a JNI based flashcard app using the OpenAI API to generate topic flashcards, with JSON-structured cards and a FastAPI interface for local run and cloud deployment.
1.9 Run Flashcard App to Local Machine2:58
1.10 Deploy it into Vercel Cloud7:28
Deploy your flashcard app to the Vercel cloud by configuring vercel.json, using the Vercel CLI, and debugging build and runtime logs; resolve import errors by updating pyproject.toml with missing libraries.

2.1 Docker installation in local machine5:55
2.2 Getting started with Docker4:43
Explore getting started with Docker by pulling and running images, creating containers, and managing them on Windows, including basic commands, ports 8080/8081, and cleanup.
2.3 Build Docker image and run it locally11:05
Build and run a dockerized python fastapi app by creating a dockerfile with python 3.11 slim, copying pyproject.toml and env, installing dependencies, and exposing port 9000.

3.1 Different GCP Services for Deployment1:48
3.2 Create Artifact Registry5:44
Create a Google Cloud artifact registry, configure it for docker images, and push the flash app image (dev, v1) for deployment to Google Cloud Run in Mumbai.
3.3 Push Docker image to Artifact Registry8:28
Learn to push a docker image to Google artifact registry using the gcloud CLI. Authenticate, configure docker, tag with a full artifact path, and push to the registry for deployment.
3.4 Deploy App to Google Cloud Run3:40
Deploy a Docker image from Artifact Registry to Google Cloud Run, expose port 9000, enable public access, and manage region and API key changes without redeploy.

Introduction to Flask & Setup environment1:58
Explore the Flask web framework in Python and set up a local environment with Anaconda or Python, create a virtual environment, and install pandas, numpy, and Flask.
Download and Install Anaconda3:33
Create Virtual environment1:54
Install Library2:35
Activate the virtual environment, install essential libraries with pip or conda, and verify by importing numpy, pandas, scikit-learn, and flask, preparing for machine learning deployment.
Spyder IDE7:50
Use the conda environment to launch the Spyder IDE and run Python via the IPython kernel, mastering the editor and work directory.
Flask Introduction6:22
(Hands-on) Flask Hello World4:59
(Hands-on) Flask Web app - With parameter8:51
Build a Flask app with get and post endpoints that accept parameters A and B, return their sum, and demonstrate parameter handling via query strings and JSON, tested with Postman.
Quiz

Section : Introduction1:53
Data Preparation & Create Model7:29
Create a logistic regression model on the iris dataset using sepal and petal features to classify iris species, use 80/20 split, achieve 96% accuracy, and prepare for serialization and deployment.
(Hands-on) Serialize & Deserialize Scikit-learn Model7:03
(Hands-on) Deploying model to Flask Web application6:56
Test Webservice through Postman +Python requests4:59
Test your deployed web service with Postman and Python requests, sending JSON data to the model endpoint and validating the iris virginica response.

Requirements

Basic Python knowledge (functions, variables)
Basic command-line familiarity
A laptop or desktop with internet access

Description

Build. Containerize. Deploy. Scale.

In today’s job market, it’s not enough to just build applications — you must know how to deploy them in the real world.

In this course, you will learn how to build a complete Generative AI web application using Python, package it using Docker, and deploy it across multiple cloud platforms including Vercel, Google Cloud Run, and Microsoft Azure.

This is not a theory-heavy course.

You will build a real AI-powered Flashcard Generator application step by step — exactly the kind of project companies expect developers, AI engineers, and DevOps professionals to know how to deploy.

Why This Course?

Most courses teach:

Only AI concepts
Or only Docker basics
Or only one cloud platform

This course connects everything together.

You will learn:

How a Generative AI application is built
How it is containerized
How it is deployed to different clouds
And how deployment strategies differ across platforms

By the end, you’ll have hands-on, production-style deployment experience, not just certificates.

What You Will Build

Throughout the course, you will build:

A Python-based Generative AI Flashcard Web Application
Uses OpenAI API to generate intelligent flashcards
Runs locally on your machine
Deployed to Vercel (serverless)
Containerized using Docker
Deployed to Google Cloud Run
Deployed to Azure Container Instances

This project can be:

Added to your portfolio
Used as a template for future AI apps
Extended into larger AI-powered systems

What You Will Learn (In Detail)

Application Development

Setting up Python and VS Code for web development
Creating a “Hello World” Python web app
Structuring a real-world Python AI project
Integrating OpenAI APIs securely
Running and testing applications locally

Containerization with Docker

Installing Docker on your local machine
Understanding Docker images, containers, and layers
Writing a Dockerfile for a Python web application
Building Docker images locally
Running and debugging containers

Deployment to Vercel

Understanding serverless deployments
Deploying a Python web application to Vercel
Managing environment variables securely
Testing your live application

Deployment to Google Cloud

Overview of GCP deployment services
Creating and using Google Artifact Registry
Pushing Docker images to Google Cloud
Deploying containerized apps to Google Cloud Run
Understanding Cloud Run scaling and pricing basics

Deployment to Azure

Overview of Azure deployment services
Creating Azure Container Registry
Pushing Docker images to Azure
Deploying containers to Azure Container Instances
Comparing Azure deployment with Google Cloud Run

Multi-Cloud Deployment Strategy

Differences between Vercel, GCP, and Azure deployments
When to choose which platform
Real-world deployment decision-making

If you want to go from “I can build apps” to “I can deploy apps in production”, this course is for you.

You will be guided step by step through:

Docker
Cloud setup
Deployment workflows

Free accounts will be required for:

Vercel
Google Cloud
Microsoft Azure
OpenAI

By the End of This Course, You Will Be Able To

Build and deploy a Generative AI web application Confidently use Docker for Python applications Deploy applications on Vercel, Google Cloud, and Azure Understand real-world cloud deployment workflows, Add a multi-cloud GenAI project to your portfolio Speak confidently about deployment in interviews

Why This Course Is Different

Build one real application, not random demos
Covers Vercel + Google Cloud + Azure in a single course
Strong focus on hands-on deployment, not just theory
Perfect bridge between AI development and cloud deployment

This course comes with 30 days money back guarantee. No question ask. So what are you waiting for just enroll it today.

I will see you inside class.

Happy learning

Ankit Mistry

Who this course is for:

Python developers who want real deployment skills
Generative AI learners who want to deploy AI apps, not just build them
Beginners in Docker and Cloud who want hands-on experience
DevOps beginners looking for practical container deployment
Students and professionals preparing for cloud or AI roles

Deploy a LLM Application to Vercel, Google Cloud & Azure

What you'll learn

Explore related topics

Course content

Getting started10 lectures • 54min

Containerization3 lectures • 22min

Deployment to Google Cloud4 lectures • 20min

Deployment to Azure Cloud4 lectures • 20min

Introduction7 lectures • 13min

Code Download1 lecture • 1min

Flask Basics8 lectures • 38min

Deploying machine learning (Sci-kit Learn) model to Flask5 lectures • 28min

Model Serialization with Tensorflow 2.02 lectures • 11min

-------- Deploy model on Heroku Cloud --------1 lecture • 12min

Requirements

Description

Who this course is for: