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Posted on Jun 8 • Originally published at towardsdev.com on May 12

Deploy Open Notebook on AWS: A Complete Step-by-Step Guide to Launch Your AI Podcast Creator

#virtualmachine #amazonwebservices #aws #aipodcast

Artificial Intelligence is transforming the way creators produce content, and podcasts are no exception. From automated research and script generation to voice synthesis and multilingual narration, AI-powered podcast creation tools are making professional-quality audio production accessible to everyone. One such powerful platform is Open Notebook — an all-in-one AI workspace that combines notebook-style research, source management, LLM integrations, and AI podcast generation into a single environment.

Deploying Open Notebook locally can sometimes be challenging due to hardware limitations, GPU requirements, dependency management, and model setup complexities. That’s where Amazon Web Services (AWS) comes in. By launching the pre-configured Open Notebook VM directly from the AWS Marketplace, you can skip the complicated setup process and instantly access a production-ready AI podcast creation environment with pre-installed Ollama models, desktop access, and a fully configured web interface.

In this guide, you’ll learn how to deploy Open Notebook on AWS step by step — from subscribing to the AWS Marketplace listing and launching the EC2 instance to configuring Ollama models, accessing the web dashboard, and generating your first AI-powered podcast. Whether you’re a content creator, AI enthusiast, researcher, educator, or developer, this tutorial will help you get your own cloud-based AI podcast studio running in minutes.

By the end of this tutorial, you’ll have:

A fully deployed Open Notebook AI environment on AWS
Secure SSH and RDP access to your VM
Configured Ollama language and embedding models
Access to the Open Notebook web interface
The ability to generate AI-powered podcasts directly from your notebooks and sources

Let’s get started and build your AI-powered podcast creation setup on AWS.

Step-by-Step Guide

Open Open Notebook — The AI Podcast creator VM listing on AWS marketplace.

Click on View purchase options.

Log in with your credentials and follow the instructions.
Review the prices and subscribe to the product by clicking on the subscribe button located at the bottom of this page. Once you are subscribed to the offer, click on the " Launch your software button.

The next page will show you the options to launch the instance: Launch through EC2 and One-click launch from AWS Marketplace. Tick the 2nd option, One-click launch from AWS Marketplace.

Select a Region where you want to launch the VM(such as US East (N.Virginia))
Optionally change the EC2 instance type. (This defaults to t2.xlarge instance type, 4 vCPUs, and 16 GB RAM.)

Optionally change the network name and subnetwork names.

Select the Security Group. Be sure that whichever Security Group you specify exposes ports 22 (for SSH), 3389 (for RDP), 80 (for HTTP), and 443 (for HTTPS). Or you can create the new SG by clicking on the “Create Security Group” button. Provide the name and description, and save the SG for this instance.

Be sure to download the key pair that is available by default, or create a new key pair and download it.
Click on Launch.
Open Notebook — The AI Podcast creator will begin deploying.

A summary page displays. To see this instance on the EC2 Console, click on the View instance on EC2 link.

To connect to this instance through Putty, copy the IPv4 Public IP Address from the VM’s details page.

Open Putty, paste the IP address and browse your private key you downloaded while deploying the VM, by going to SSH->Auth->Credentials, click on Open. Enter ubuntu as the userid

Once connected, change the password for the Ubuntu user using the command below

sudo passwd ubuntu

Now that the password for the Ubuntu user is set, you can connect to the VM’s desktop environment from any local Windows Machine using RDP protocol or Linux Machine using Remmina.

From your local Windows machine, goto “Start” menu, in the search box type and select “Remote Desktop Connection”. In the “Remote Desktop Connection” wizard, copy the public IP address and click Connect

This will connect you to the VM’s desktop environment. Provide the username “ubuntu” and the password set in the above “Reset password” step to authenticate. Click OK

Now you are connected to the out-of-the-box Open Notebook — The AI Podcast creator VM’s desktop environment via Windows Machine.

To connect using RDP via a Linux machine, first note the external IP of the VM from the VM details page, then from your local Linux machine, goto menu, in the search box type and select “Remmina”.

Note: If you don’t have Remmina installed on your Linux machine, first install Remmina as per your Linux distribution.

In the “Remmina Remote Desktop Client” wizard, select the RDP option from the dropdown and paste the external IP, and click Enter.

This will connect you to the VM’s desktop environment. Provide “ubuntu” as the username and the password set in the above reset password step to authenticate. Click OK

Now you are connected to the out-of-the-box Open Notebook — The AI Podcast creator VM’s desktop environment via a Linux machine.

The VM will generate a random password to log in to the Open Notebook Web Interface as well as an encryption key for database access. To get the password, connect via SSH terminal as shown in the above steps and run the command below.

cat /home/ubuntu/open-notebook-local/open_notebook_credentials.txt

To access the Open Notebook Web Interface, copy the public IP address of the VM and paste it in your local browser as https://public_ip_of_vm. Make sure to use https and not http.

The browser will display an SSL certificate warning message. Expand the warning message, accept the certificate warning, and continue.

It will open a login page. Provide the password we got at step 14 above and click Sign In.

Now you are logged in to the Open Notebook Web Interface.

The VM comes with “Ollama setup” and few models are already pulled for you to get started. To begin with the Open Notebook, first you need to configure the ollama models. To do so, click on the Models option from the left menu and scroll down to the Ollama section. Click on the Add Configuration button.

Give a name to your configuration, leave API Key blank, and for Base URL enter http://ollama:11434. As the ollama is running in a container, make sure to enter Base URL as http://ollama:11434 and not http://localhost:11434.

Scroll up, and you should see ollama successfully configured. Next, we need to add language and embedding models. For that, click on the models option as shown in the screenshot below.

Select Model Type as Language. By default, the following ollama models are already pulled on this VM. Select language model(s) from the list of available models.

Selected models will get added as shown below. Similarly, again click on the models option to add embedding models. Select Model Type as Embedding and tick mxbai-embed-large: latest from the list of available models.

Note: If you want to go with other LLM providers like OpenAI/Anthropic, etc., then configure the same from this Models page.

Now that we have configured the ollama models, we need to set the default models. On the same Models page, scroll up to the top of the Default Model Assignments section. Select various models from the dropdown.

Now you are all set to use Open Notebook. The basic workflow includes creating a new notebook with a detailed description (as a detailed description helps LLM to understand the context of the notebook and provide you with better answers), adding sources, gathering insights from the added sources using transformations, and finally talking to the Assistant.

To add the sources, click on New from the left menu and select the Sources option. Choose from various options.

Click on Next to select the Notebook for these sources.

Click on Next and lastly select the Transformation option.

You can see the list of added sources here.

Once your sources are ready, you can use the notebook, Ask and Search, and Podcast features.

For the podcast feature, you will need some extra configuration. You will need to update the podcast profiles and need to provide the various models, including voice. Once it is ready, go to the podcast tab from the left pane, click on Generate podcast and provide the details here. For more details, please refer to the official documentation here

Lastly, to pull more ollama models on this VM, connect via SSH terminal and run below command.

sudo docker exec open-notebook-local-ollama-1 ollama pull <modelname>

e.g sudo docker exec open-notebook-local-ollama-1 ollama pull qwen2.5:latest

For more details, please visit the Official Documentation page

Conclusion

Deploying Open Notebook on Amazon Web Services provides a powerful and scalable way to create AI-driven podcasts without dealing with complicated local installations or hardware limitations. With the pre-configured AWS Marketplace image, you get a ready-to-use environment that includes Ollama integration, preloaded AI models, remote desktop access, and a fully functional web interface designed for research, content generation, and podcast creation.

Throughout this guide, we covered the complete deployment workflow — from launching the EC2 instance and configuring security groups to accessing the Open Notebook dashboard, setting up Ollama models, and generating AI podcasts from your own sources and notebooks. The platform’s flexibility also allows you to integrate additional LLM providers such as OpenAI or Anthropic, making it suitable for a wide range of AI content workflows beyond podcasting.

As AI-generated media continues to evolve, tools like Open Notebook are enabling creators, educators, developers, and businesses to automate large parts of the content production pipeline while maintaining creative control. Running it on AWS gives you the added benefits of scalability, remote accessibility, and the ability to experiment with larger models and workloads whenever needed.

Now that your deployment is complete, you can start exploring advanced workflows such as: